Conference Paper2012Cited by 123

Dermatological disease diagnosis using color-skin images

M Shamsul Arifin, M Golam Kibria, Adnan Firoze, M Ashraful Amini, Hong Yan

2012 International Conference on Machine Learning and Cybernetics

IEEE, Vol. 5, pp. 1675–1680

  • Good Health and Well-being — Relevance: 48%

Abstract

This paper presents an automated dermatological diagnostic system. Etymologically, dermatology is the medical discipline of analysis and treatment of skin anomalies. The system presented is a machine intervention in contrast to human arbitration into the conventional medical personnel based ideology of dermatological diagnosis. The system works on two dependent steps - the first detects skin anomalies and the latter identifies the diseases. The system operates on visual input i.e. high resolution color images and patient history. In terms of machine intervention, the system uses color image processing techniques, k-means clustering and color gradient techniques to identify the diseased skin. For disease classification, the system resorts to feedforward backpropagation artificial neural networks. The system exhibits a diseased skin detection accuracy of 95.99% and disease identification accuracy of 94.016% while tested for a total of 2055 diseased areas in 704 skin images for 6 diseases.

Keywords

  • K-means clustering
  • BP neural network
  • dermatological diagnosis
  • color image processing
  • skin disease detection
  • color gradients
  • skin lesion classification
  • medical history
  • automated diagnosis

References

  1. 1.S Agatonovic-Kustrin, R Beresford. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727[10.1016/s0731-7085(99)00272-1]
  2. 2.Chris M. Bishop. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803–1832[10.1063/1.1144830]
  3. 3.Monika Gniadecka, Peter Alshede Philipsen, Sonja Wessel, Robert Gniadecki, Hans Christian Wulf, Sigurdur Sigurdsson, Ole Faurskov Nielsen, Daniel Højgaard Christensen, Jana Hercogova, Kristian Rossen, Henrik Klem Thomsen, Lars Kai Hansen. (2004). Melanoma Diagnosis by Raman Spectroscopy and Neural Networks: Structure Alterations in Proteins and Lipids in Intact Cancer Tissue. Journal of Investigative Dermatology, 122(2), 443–449[10.1046/j.0022-202x.2004.22208.x]
  4. 4.Matthew G. Fleming, Carsten Steger, Jun Zhang, Jianbo Gao, Armand B. Cognetta, llya Pollak, Charles R. Dyer. (1998). Techniques for a structural analysis of dermatoscopic imagery. Computerized Medical Imaging and Graphics, 22(5), 375–389[10.1016/s0895-6111(98)00048-2]
  5. 5.H. Handels, Th. Roß, J. Kreusch, H.H. Wolff, S.J. Pöppl. (1999). Feature selection for optimized skin tumor recognition using genetic algorithms. Artificial Intelligence in Medicine, 16(3), 283–297[10.1016/s0933-3657(99)00005-6]
  6. 6.K. Hoffmann, T. Gambichler, A. Rick, M. Kreutz, M. Anschuetz, T. Grunendick, A. Orlikov, S. Gehlen, R. Perotti, L. Andreassi, J. Newton Bishop, J-P. Cesarini, T. Fischer, P.J. Frosch, R. Lindskov, R. Mackie, D. Nashan, A. Sommer, M. Neumann, J.P. Ortonne, P. Bahadoran, P.F. Penas, U. Zoras, P. Altmeyer. (2003). Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy. British Journal of Dermatology, 149(4), 801–809[10.1046/j.1365-2133.2003.05547.x]
  7. 7.Gillian M Murphy, Christopher Rowland Payne. (2002). Atlas of Clinical Dermatology. Journal of Cosmetic Dermatology, 1(4), 223[10.1111/j.1473-2165.2002.00058.x]
  8. 8.Jianbo Gao, Jun Zhang, M.G. Fleming, I. Pollak, A.B. Cognetta. (2002). Segmentation of dermatoscopic images by stabilized inverse diffusion equations. , 3, 823–827[10.1109/icip.1998.727381]
  9. 9.Kenneth R. Castleman. (2002). Color Image Processing. [10.1002/0471443395.img006]

Cited By

  1. 1.Nawal Soliman ALKolifi ALEnezi. (2019). A Method Of Skin Disease Detection Using Image Processing And Machine Learning. Procedia Computer Science, 163, 85–92[10.1016/j.procs.2019.12.090]
  2. 2.Nazia Hameed, Antesar M. Shabut, Miltu K. Ghosh, M.A. Hossain. (2019). Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Systems with Applications, 141, 112961[10.1016/j.eswa.2019.112961]
  3. 3.Vijayalakshmi M M. (2019). Melanoma Skin Cancer Detection using Image Processing and Machine Learning. International Journal of Trend in Scientific Research and Development, Volume-3(Issue-4), 780–784[10.31142/ijtsrd23936]
  4. 4.Rahat Yasir, Md. Ashiqur Rahman, Nova Ahmed. (2014). Dermatological disease detection using image processing and artificial neural network. , 687–690[10.1109/icece.2014.7026918]
  5. 5.R Karthik, Tejas Sunil Vaichole, Sanika Kiran Kulkarni, Ojaswa Yadav, Faiz Khan. (2021). Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification. Biomedical Signal Processing and Control, 73, 103406[10.1016/j.bspc.2021.103406]
  6. 6.Nazia Hameed, Antesar M. Shabut, M. A. Hossain. (2018). Multi-Class Skin Diseases Classification Using Deep Convolutional Neural Network and Support Vector Machine. , 1–7[10.1109/skima.2018.8631525]
  7. 7.Doaa Sami Khafaga, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Faten Khalid Karim, Seyedali Mirjalili, Nima Khodadadi, Wei Hong Lim, Marwa M. Eid, Mohamed E. Ghoneim. (2022). An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease. Diagnostics, 12(11), 2892[10.3390/diagnostics12112892]
  8. 8.Khalid M. Hosny, Mohamed A. Kassem, Mohamed M. Foaud. (2020). Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks. Multimedia Tools and Applications, 79(33-34), 24029–24055[10.1007/s11042-020-09067-2]
  9. 9.(2024). SKIN CANCER DETECTION USING IMAGE PROCESSING. International Research Journal of Modernization in Engineering Technology and Science[10.56726/irjmets52335]
  10. 10.Youssef Filali, Hasnae EL Khoukhi, My Abdelouahed Sabri, Abdellah Aarab. (2020). Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer. Multimedia Tools and Applications, 79(41-42), 31219–31238[10.1007/s11042-020-09637-4]
  11. 11.Sourav Kumar Patnaik, Mansher Singh Sidhu, Yaagyanika Gehlot, Bhairvi Sharma, P. Muthu. (2018). utomated Skin Disease Identification using Deep Learning Algorithm. Biomedical & Pharmacology Journal, 11(3), 1429–1436[10.13005/bpj/1507]
  12. 12.Palak Mehta, Bhumika Shah. (2016). Review on Techniques and Steps of Computer Aided Skin Cancer Diagnosis. Procedia Computer Science, 85, 309–316[10.1016/j.procs.2016.05.238]
  13. 13.Tanvi Goswami, Vipul K. Dabhi, Harshadkumar B. Prajapati. (2020). Skin Disease Classification from Image - A Survey. , 599–605[10.1109/icaccs48705.2020.9074232]
  14. 14.Launcelot C. De Guzman, Ryan Paolo C. Maglaque, Vianca May B. Torres, Simon Philippe A. Zapido, Macario O. Cordel. (2015). Design and Evaluation of a Multi-model, Multi-level Artificial Neural Network for Eczema Skin Lesion Detection. , 42–47[10.1109/aims.2015.17]
  15. 15.Lifang Guo, Yin Yang, Hui Ding, Huiying Zheng, Hedan Yang, Junxiang Xie, Yong Li, Tong Lin, Yiping Ge. (2022). A deep learning-based hybrid artificial intelligence model for the detection and severity assessment of vitiligo lesions. Annals of Translational Medicine, 10(10), 590[10.21037/atm-22-1738]
  16. 16.Parameshwar R. Hegde, Manjunath M. Shenoy, B.H. Shekar. (2018). Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features. , 1825–1828[10.1109/icacci.2018.8554512]
  17. 17.K. Veera Swamy, B. Divya. (2021). Skin Disease Classification using Machine Learning Algorithms. , 1–5[10.1109/c2i454156.2021.9689338]
  18. 18.Nikos Petrellis. (2018). A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis. Symmetry, 10(7), 270[10.3390/sym10070270]
  19. 19.Yasha Hasija, Nikhil Garg, Soumya Sourav. (2017). Automated detection of dermatological disorders through image-processing and machine learning. , 1047–1051[10.1109/iss1.2017.8389340]
  20. 20.Francisco J. Veredas, Héctor Mesa, Laura Morente. (2015). Efficient detection of wound-bed and peripheral skin with statistical colour models. Medical & Biological Engineering & Computing, 53(4), 345–359[10.1007/s11517-014-1240-0]
  21. 21.Sezin Barın, Gür Emre Güraksın. (2022). An automatic skin lesion segmentation system with hybrid FCN-ResAlexNet. Engineering Science and Technology an International Journal, 34, 101174[10.1016/j.jestch.2022.101174]
  22. 22.Moolchand Sharma, Bhanu Jain, Chetan Kargeti, Vinayak Gupta, Deepak Gupta. (2020). Detection and Diagnosis of Skin Diseases Using Residual Neural Networks (RESNET). International Journal of Image and Graphics, 21(05)[10.1142/s0219467821400027]
  23. 23.Lakshay Bajaj, Himanshu Kumar, Yasha Hasija. (2018). Automated System for Prediction of Skin Disease using Image Processing and Machine Learning. International Journal of Computer Applications, 180(19), 9–12[10.5120/ijca2018916428]
  24. 24.V. S. S. Bala Tripura Sathvika, Nagilla Anmisha, Vada Thanmayi, M. Suchetha, D. Edwin Dhas, S. Sehastrajit, Sathyanarayanan N. Aakur. (2024). Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features. IEEE Access, 12, 57366–57380[10.1109/access.2024.3387533]
  25. 25.Mohamed S. Elmahdy, Sara S. Abdeldayem, Inas A. Yassine. (2017). Low quality dermal image classification using transfer learning. , 373–376[10.1109/bhi.2017.7897283]
  26. 26.Anika Nawar, Noor Kibria Sabuz, Shah Md. Tanvir Siddiquee, Masud Rabbani, Al Amin Biswas, Anup Majumder. (2021). Skin Disease Recognition: A Machine Vision Based Approach. , 1029–1034[10.1109/icaccs51430.2021.9441980]
  27. 27.Nurul Akmalia, Poltak Sihombing, Suherman. (2019). Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network. , 168–173[10.1109/elticom47379.2019.8943892]
  28. 28.Sameer Dev Sharma, Sonal Sharma, Abhishek Kumar Pathak, Nachaat Mohamed. (2023). Real-time Skin Disease Prediction System using Deep Learning Approach. , 1–6[10.1109/delcon57910.2023.10127569]
  29. 29.Matthew Groh, Caleb Harris, Luis Soenksen, Felix Lau, Rachel Han, Aerin Kim, Arash Koochek, Omar Badri. (2021). Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset. , 1820–1828[10.1109/cvprw53098.2021.00201]
  30. 30.P. Nagaraj, V. Muneeswaran, K. Jaya Krishna, K. Yerriswamy Reddy, J. Rock Morries, G. Pavan Kumar. (2022). Identification of Skin Diseases using a Novel Deep CNN. 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 992–997[10.1109/icesc54411.2022.9885330]
  31. 31.Syeda Shamaila Zareen, Sun Guangmin, Yu Li, Mahwish Kundi, Salman Qadri, Syed Furqan Qadri, Mubashir Ahmad, Ali Haider Khan. (2022). A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features. Computational Intelligence and Neuroscience, 2022, 1–11[10.1155/2022/4942637]
  32. 32.Koteswara Rao Kodepogu, Jagadeeswara Rao Annam, Aruna Vipparla, Bala Venkata Naga Vudata Suresh Krishna, Naresh Kumar, Ramya Viswanathan, Lalitha Kumari Gaddala, Suresh Kumar Chandanapalli. (2022). A Novel Deep Convolutional Neural Network for Diagnosis of Skin Disease. Traitement du signal, 39(5), 1873–1877[10.18280/ts.390548]
  33. 33.Md. Al Mamun, Mohammad Shorif Uddin. (2021). A Survey on a Skin Disease Detection System. International Journal of Healthcare Information Systems and Informatics, 16(4), 1–17[10.4018/ijhisi.20211001.oa35]
  34. 34.Amina Aboulmira, El Mehdi Raouhi, Hamid Hrimech, Mohamed Lachgar. (2022). Ensemble learning methods for deep learning: Application to skin lesions classification. , 1–6[10.1109/isivc54825.2022.9800732]
  35. 35.Amer Sallam, Abdulfattah E. Ba Alawi. (2019). Mobile-based Intelligent Skin Diseases Diagnosis System. , 1–6[10.1109/icoice48418.2019.9035129]
  36. 36.Md. Humayan Ahmed, Romana Rahman Ema, Tajul Islam. (2019). An Automated Dermatological Images Segmentation Based on a New Hybrid Intelligent ACO-GA Algorithm and Diseases Identification Using TSVM Classifier. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 1–6[10.1109/icasert.2019.8934560]
  37. 37.Tatyana Polevaya, Roman Ravodin, Andrey Filchenkov. (2019). Skin Lesion Primary Morphology Classification With End-To-End Deep Learning Network. , 247–250[10.1109/icaiic.2019.8668980]
  38. 38.Jyothilakshmi K. K, Jeeva J. B. (2014). Detection of malignant skin diseases based on the lesion segmentation. , 382–386[10.1109/iccsp.2014.6949867]
  39. 39.Jihan Alam. (2021). An Efficient Approach for Skin Disease Detection using Deep Learning. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1–8[10.1109/csde53843.2021.9718427]
  40. 40.Gowravi Malalur Rajegowda, Yannis Spyridis, Barbara Villarini, Vasileios Argyriou. (2024). An AI-Assisted Skincare Routine Recommendation System in XR. Smart innovation, systems and technologies, 381–395[10.1007/978-981-99-9018-4_28]
  41. 41.Yuvika Gautam, Piyush Gupta, Deepika Kumar, Bhavya Kalra, Abhinav Kumar, Jude D. Hemanth. (2024). FusionEXNet: an interpretable fused deep learning model for skin cancer detection. International Journal of Computers and Applications, 46(9), 743–753[10.1080/1206212x.2024.2385923]
  42. 42.Arnab Banerjee, Somenath Sarkar, Mita Nasipuri, Nibaran Das. (2023). Skin Diseases Detection Using LBP and WLD: An Ensembling Approach. SN Computer Science, 5(1)[10.1007/s42979-023-02395-8]
  43. 43.Mustaqeem Khan, Jamil Ahmad, Abdulmotaleb El Saddik, Wail Gueaieb. (2024). Skin-Former: Mobile-Friendly Transformer for Skin Lesion Diagnosis. , 1–6[10.1109/icce59016.2024.10444175]
  44. 44.Yingjie Xia, Luming Zhang, Lei Meng, Yan Yan, Liqiang Nie, Xuelong Li. (2017). Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework. IEEE Transactions on Cybernetics, 48(11), 3080–3091[10.1109/tcyb.2017.2765665]
  45. 45.Fahima Hasan Athina, Sadaf Ahmed Sara, Quazi Sabrina Sarwar, Nishat Tabassum, Mun Tarin Jannat Era, Faisal Bin Ashraf, Muhammad Iqbal Hossain. (2022). Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI. 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 648–655[10.1109/3ict56508.2022.9990755]
  46. 46.Nimra Shehzadi, Arshia Rehman, Saeeda Naz, Saba Rehman, Fahmi Khalifa. (2025). Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future. Artificial Intelligence Review, 58(9)[10.1007/s10462-025-11195-w]
  47. 47.Guillermo Peris Fajarnés, María Moncho Santonja, Beatriz Defez García, Ismael Lengua Lengua. (2020). Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. Skin Research and Technology, 26(5), 734–739[10.1111/srt.12865]
  48. 48.Nihar Abhvankar, Himanshu Pingulkar, Kartik Chindarkar, Asma Parveen I. Siddavatam. (2021). Detection of Melanoma and Non-Melanoma type of Skin Cancer using CNN and RESNET. 2021 Asian Conference on Innovation in Technology (ASIANCON)[10.1109/asiancon51346.2021.9544656]
  49. 49.María Moncho-Santonja, Silvia Aparisi-Navarro, Beatriz Defez, Guillermo Peris-Fajarnés. (2023). Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study. Applied Sciences, 13(10), 6157[10.3390/app13106157]
  50. 50.Nagaraj P, Suresh Kumar S, V. Saijagadeeshkumar, G. Pavan Kumar, K. Yerriswamyreddy, K. Jaya Krishna. (2023). Skin Cancer Detection and Control Techniques Using Hybrid Deep Learning Techniques. , 442–446[10.1109/icpcsn58827.2023.00078]
  51. 51.Chowdary, G Jignesh, G, Suganya, M, Premalatha, Y, Asnath Victy Phamila, K, Karunamurthy. (2021). Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey. arXiv (Cornell University)[10.48550/arxiv.2107.14037]
  52. 52.Anuj Singh, Nithin Kumar K C, M. Anand Kumar, Harendra Singh Negi. (2023). An Improved Deep Learning Framework Approach for Detecting the Psoriasis Disease. , 1–5[10.1109/iceconf57129.2023.10083536]
  53. 53.Prathamesh Raut, Narottam Sahu, Avirat Sudam, Aman Shrivastava, Snehal Mumbaikar. (2023). Skin Lesion Detection Using MobileNet CNN. , 1–6[10.1109/asiancon58793.2023.10270171]
  54. 54.Md. Ariful Islam, Mohammad Shamsul Arefin. (2016). A framework for detecting arsenic disease. , 1–5[10.1109/ceeict.2016.7873149]
  55. 55.Venura Nimesh, Rukshala Weerasinghe. (2021). Differential Diagnosis of Ringworm and Eczema Using Image Processing and Deep Learning. , 147–152[10.1109/icter53630.2021.9774803]
  56. 56.Bassma, Rizuan Ahmed Rifat, Md. Kafiul Islam. (2022). Skin Disease Screening System Based on Smartphone Captured Images Using Deep Learning. , 267–272[10.1109/icccas55266.2022.9824580]
  57. 57.Ishita Bhakta, Santanu Phadikar, Koushik Majumder, Arkaprabha Sau, Sanjib Chowdhuri. (2019). Tsalli’s Entropy-based Segmentation Method for Accurate Pigmented Skin Lesion Identification. IETE Journal of Research, 68(1), 743–759[10.1080/03772063.2019.1622459]
  58. 58.Harsh Gunwant, Abhisht Joshi, Moolchand Sharma, Deepak Gupta. (2022). Automated Medical Diagnosis and Classification of Skin Diseases Using Efficinetnet-B0 Convolutional Neural Network. Studies in computational intelligence, 3–19[10.1007/978-3-031-08266-5_1]
  59. 59.Sudarshan Nath, Suparna Das Gupta, Soumyabrata Saha. (2023). Deep learning-based common skin disease image classification. Journal of Intelligent & Fuzzy Systems, 44(5), 7483–7499[10.3233/jifs-222773]
  60. 60.Ajaykumar Dharmireddy, A. Chakradhar, Sk. Vaseem Akram, R. Sai Deepak, S. Akash, T. Rajasekhar, T. Anatha. (2024). Dermatological disease detection and preventative measures using deep convolution neural networks. AIP conference proceedings, 3170, 030014[10.1063/5.0195880]
  61. 61.Priyanka Saraf, P R Tharaniesh, Soumyendra Singh. (2024). Skin Disease Detection using Convolutional Neural Network. , 1–6[10.1109/nmitcon62075.2024.10699196]
  62. 62.Fahim Muntasir, Mir Arfan Uddin, Nabajit Kumar Dey Utsab, Jamil As-ad, Fariha Tasnim. (2024). A grad-CAM and deep learning based image classification for skin diseases. , 801–808[10.1145/3723178.3723284]
  63. 63.Kushagra Agrawal, Shaveta Jain. (2023). Machine Learning Enabled Model for Different Skin Disease Prediction. , 1531–1536[10.1109/ictacs59847.2023.10389851]
  64. 64.Uvin Withana, Pumudu Fernando. (2017). Differential diagnosis of eczema and psoriasis using categorical data in image processing. , 1–6[10.1109/icter.2017.8257819]
  65. 65.Minakshi M. Sonawane, Ramdas D. Gore, Bharti W. Gawali, Ramesh R. Manza, Sudhir N. Mendhekar. (2023). Identification of Skin Disease Using Machine Learning. Advances in intelligent systems research/Advances in Intelligent Systems Research, 99–113[10.2991/978-94-6463-196-8_9]
  66. 66.Abraham Evwiekpaefe, Oghenegueke Amrevuawho. (2023). CLASSIFICATION OF DERMATOLOGIC MANIFESTATIONS OF CARDIOVASCULAR DISEASE USING EFFICIENTNETV2 CNN MODEL. International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences, 23(1), 115–127[10.21608/ijicis.2023.184311.1242]
  67. 67.Radha Yadav, Saket Kumar Choudhary. (2022). framework to detect skin disease using deep learning techniques. International Journal of Health Sciences, 7260–7270[10.53730/ijhs.v6ns5.10331]
  68. 68.B. Suman, N. Harika, B. Sruthi, B. Bhagyasree. (2022). Prediction of Skin Diseases Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 10(8), 791–796[10.22214/ijraset.2022.46138]
  69. 69.syeda shumaila zareen, Sun Guangmin, Yu Li, Salman Qadri, Syed Usman Qadri. (2022). A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features. SSRN Electronic Journal[10.2139/ssrn.4002124]
  70. 70.Summi Goindi, Khushal Thakur, Divneet Singh Kapoor. (2023). Skin Disease Classification and Detection by Deep Learning and Machine Learning Approaches. Lecture notes in networks and systems, 147–162[10.1007/978-981-99-5085-0_16]
  71. 71.V. Ebenezer, Sachin R, S. S. Hiruthic, S. Sam Sergius, R Shivnesh. (2023). SDNS: Artificial Neural Network Scheme to Solve the Nonlinear Skin Disease Model. , 10, 931–935[10.1109/icears56392.2023.10085024]
  72. 72.G. Glorindal, S. Arun Mozhiselvi, T. Ananth Kumar, K. Kumaran, Phillip Chisomo Katema, Timothy Kandimba. (2021). A Simplified Approach for Melanoma Skin Disease Identification. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 1–5[10.1109/icscan53069.2021.9526511]
  73. 73.B. Veerasekhar Reddy, T. Venkata Nagaraju, K. Laya, Shahzeen Fatima, T. Charan, Ch. Vagdevi. (2023). Retracted: Skin Disease Classification Using Machine Learning. , 1–10[10.1109/icccnt56998.2023.10306716]
  74. 74.V. Rakesh, D. Abhishek, O. Earni Sai, Y. S. H. S. Rohit, R. Venkata Ramana. (2024). Skin Disease Detection Using Deep Learning. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 201–208[10.32628/cseit2410134]
  75. 75.Kurnia Zikir Bimastro, Tito Waluyo Purboyo, Casi Setianingsih, Muhammad Ary Murti. (2019). Potential Detection of Lentigo Maligna Melanoma on Solar Lentigines Image Based on Android. , iii, 113–118[10.1109/icitisee48480.2019.9003743]
  76. 76.Minakshi M. Sonawane, Bharti W. Gawali, Ramesh R. Manza, Sudhir Mendhekar. (2022). Analysis of Skin disease techniques using Smart Phone and Digital Camera Identification of Skin Disease. Research Journal of Science and Technology, 145–155[10.52711/2349-2988.2022.00024]
  77. 77.Monishanker Halder, Hussain Moh. Emrul Kabir, Afsana Mimi Rity, Arobindo Vowmik. (2022). An automated detection of Scabies skin disease Using Image Processing and CNN. , 1–6[10.1109/sti56238.2022.10103250]
  78. 78.Alok Kumar Moharana, Daxa Vekariya. (2022). Retracted: Detection of Skin Diseases via Deep Learning using SVM Method. , 1358–1363[10.1109/smart55829.2022.10047402]
  79. 79.D. Vishal, M. Venkatesh Manikandaprabhu, B. Vishnuvardhan, S. Yuvaraj. (2024). A study on machine learning models for segmentation and classification of skin diseases. AIP conference proceedings, 3087, 020012[10.1063/5.0194594]
  80. 80.(2023). Skin Disease Detection Based On Deep Learning. Journal of Population Therapeutics and Clinical Pharmacology, 30(6)[10.47750/jptcp.2023.30.06.045]
  81. 81.Arfa Skandar, Zainab Asim, Tayyaba Arbab, Naila Batool. (2025). DermInsight: A diagnostic System for Human Skin Diseases utilizing Deep Learning. VAWKUM Transactions on Computer Sciences, 13(1), 120–139[10.21015/vtcs.v13i1.2021]
  82. 82.P. Ananthi, Sukant A. K. (2025). Federated Learning Based Skin Disease Prediction. , 1–7[10.1109/incet64471.2025.11140150]
  83. 83.Sanjana S Ghatina, Mr Santhosh S G. (2022). Skin Disease Detection Using CNN. International Journal of Advanced Research in Science Communication and Technology, 642–648[10.48175/ijarsct-5086]
  84. 84.Ashwin Kumar Gupta, Vivek Baviskar, Premdeep Sethi, Dhiraj Bhise. (2023). Application of Deep Learning in Skin Disease Diagnosis: A Review. , 1773–1780[10.1109/icict57646.2023.10134158]
  85. 85.Sonam Dubey, Kamlesh Kumar Singh, Prashant Kumar Mishra. (2024). Innovative Solutions for Skin Disease Prediction: A Comprehensive Analysis. , 1–4[10.1109/ic3tes62412.2024.10877580]
  86. 86.(2023). SKIN DISEASE DETECTION WEB APPLICATION USING IMAGE PROCESSING. International Research Journal of Modernization in Engineering Technology and Science[10.56726/irjmets42678]
  87. 87.Mrs. R Soundharya, Akanksha Shettigar, Ananya Prasad, Ashwith R Poojary, Deepa Naik. (2024). Skin Disease Detection. International Journal of Advanced Research in Science Communication and Technology, 393–401[10.48175/ijarsct-18465]
  88. 88.Sagar Janokar, Kalpesh Joshi, Rajesh Raikwar, Sahil Kumbhar, Akshay Kumbhar, Shashank Kumbhar. (2024). Skin Disease Detection. Lecture notes in electrical engineering, 76–85[10.1007/978-981-97-8031-0_9]
  89. 89.Rahama Salman, Subodhini Gupta. (2023). DeepQ classification automated disease classification in global perspective approach and predictive decision using tensor flow. World Journal of Advanced Research and Reviews, 17(2), 200–207[10.30574/wjarr.2023.17.2.0198]
  90. 90.Rajat Mishra Rajat Mishra, Vandana Dixit Kaushik. (2024). DEEP INTO ACCURACY: EXPLORING THE PERFORMANCE OF SKIN DETECTION ALGORITHMS IN DEEP LEARNING. , 104–108[10.55524/csistw.2024.12.1.19]
  91. 91.Shweta Koparde, Niket Yadav, Ayush Kejriwal, Prathamesh Adkine. (2024). Monkey Pox Detection using Deep learning. International Journal of Advanced Research in Science Communication and Technology, 223–229[10.48175/ijarsct-18329]
  92. 92.Shanthi Thirunavukkarasu, Eldho Paul, Reethika Singaravelan, Vijay Shankar Srinivasan. (2023). Skin disease detection using CNN. AIP conference proceedings, 2984, 020055[10.1063/5.0164290]
  93. 93.Naresh Thoutam. (2022). Classification of Canine Skin Disease using Deep Learning Algorithms. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 06(11)[10.55041/ijsrem16852]
  94. 94.Gaurav Kumar Gautam, Sofia Singh, Archana Singh. (2024). Skin Cancer Identification Using Deep Learning Technique. , 70, 553–559[10.1109/ic3se62002.2024.10593153]
  95. 95.T.A. Polevaya, I.A. Saitov, R.A. Ravodin, A.A. Filchenkov. (2019). Image classification of primary morphological elements of skin lesions. Scientific and technical journal of information technologies mechanics and optics, 19(2), 333–338[10.17586/2226-1494-2019-19-2-333-338]
  96. 96.G. Saidulu. (2024). Skin Lesion Detection. International Journal for Research in Applied Science and Engineering Technology, 12(3), 2304–2310[10.22214/ijraset.2024.59322]
  97. 97.Siddharth Raj Dash. (2021). Skin Cancer Analysis with Deep Learning. International Journal for Research in Applied Science and Engineering Technology, 9(VII), 2663–2672[10.22214/ijraset.2021.36761]
  98. 98.Manali Kshirsagar, Haziquddin Ansari, Himanshu Upase, Danish Ansari, Meghashree Mohane. (2023). Design of Interactive Portal for Skin Disease Detection and Live Counseling. , 1–5[10.1109/incacct57535.2023.10141781]
  99. 99.Gauri Dilip Halankar, Amita Dessai. (2021). Skin Lesion Analysis for Classifying Melano. 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), 4, 382–387[10.1109/iceeccot52851.2021.9707924]
  100. 100.Prof. Mrs. K. Chandra Prabha. (2019). Applying Texture Feature Extraction for Skin Diseases using GLCM and GLRLM. International Journal for Research in Applied Science and Engineering Technology, 7(5), 147–153[10.22214/ijraset.2019.5023]
  101. 101.Bahaa Saifalnasr Rabi Mohammed, Aliaa Elhusseiny Mohamed Daifalla. (2026). 10 Skin Multi-Diseases Classification Based on Deep Convolutional Neural Network. , 1–6[10.1109/ieec69628.2026.11584083]
  102. 102.Prathamesh Walvekar, Sonali Kadam, Vaishnavi Kesarkar, Uma P. Gurav. (2024). DERM-AI: Dermatological Disease Prediction Based on CNN Model. , 1–5[10.1109/aisp61711.2024.10870861]
  103. 103.Narinder Kaur, Parav Sharma, Dipannita Ray. (2026). Enhancing Skin Lesion Image Quality Using Non-Local Means Denoising for Improved Diagnosis. , 1–6[10.1109/icsft66733.2026.11506739]
  104. 104.Gishma K.M, Fathima Neja, Husna Parveen, Mirsa Mohammed, Thansiha T.S. (2025). Skin Disease Detection Using CNN: 6th International Conference on Innovative Trends in Information Technology(ICITIIT-2025). , 1–4[10.1109/icitiit64777.2025.11041148]
  105. 105.Md. Joynal Abedin, Jannat Rosul Nisha, Rahat Faruk, Sanjida Akter, Tamanna E Fatema Hridi, Aroni Rani Mondal Nishi. (2025). A Study of Images Based Skin Disease Recognition Using Deep Learning. , 1–6[10.1109/qpain66474.2025.11172035]
  106. 106.Guillermo Peris-Fajarnés, María Moncho Santonja, María Begoña Sanz Alamán, Beatriz Defez García, Ismael Lengua Lengua. (2019). Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. Proceedings INNODOCT/19. International Conference on Innovation, Documentation and Education[10.4995/inn2019.2019.10946]
  107. 107.Nikos Petrellis. (2019). Skin disorder diagnosis with ambiguity reduction assisted by lesion color adaptation. AIMS Electronics and Electrical Engineering, 3(3), 290–308[10.3934/electreng.2019.3.290]
  108. 108.Choo Shue Jyoe, Tan Bee Sian, Chong Kim Soon, Lim Fung Ji. (2025). Dog Health Management System : Image-Based Skin Disease Detection Using Google Cloud Vertex AI. , 1–6[10.1109/icdxa69105.2025.11329653]
  109. 109.Rahul Negi, Shivang Negi, Neha Tripathi, Siddhant Thapliyal. (2023). Investigating Different Deep learning Models for Classification of Folliculitis. , 12, 1–5[10.1109/incet57972.2023.10170315]
  110. 110.(2025). AI-Powered Skin Cancer Screening Integrating Image Processing with Deep Learning for Enhanced Accuracy. Journal on Electronic and Automation Engineering, 4(2 June 2025), 84–93[10.46632/jeae/4/2/13]
  111. 111.Ms. R Soundharya. (2024). Skin Disease Detection Model. International Journal for Research in Applied Science and Engineering Technology, 12(5), 4146–4153[10.22214/ijraset.2024.62566]
  112. 112.Shrushti Patil, Tanmay Patil, Miheet Shah, Muskan Singh, Geetanjali Kalme, Kiran Deshpande. (2025). DermIQ: An Intelligent Skin Disease Detection System using Machine Learning. , 677–682[10.1109/netcrypt65877.2025.11102342]
  113. 113.(2023). SKIN DISEASE DETECTION SYSTEM USING IMAGE PROCESSING AND DEEP LEARNING. International Research Journal of Modernization in Engineering Technology and Science[10.56726/irjmets33604]
  114. 114.Himanshu Kumar, Yasha Hasija. (2020). Machine Learning in Medical Image Processing. Smart innovation, systems and technologies, 377–383[10.1007/978-981-15-7078-0_35]
  115. 115.Mangalam Mall, Spruha Nimkar, Vishal Banik, Dhananjay Theckedath. (2023). Surveying the Field of Dermatological Disease Detection: Machine Learning and Image Analysis Perspectives. , 06, 1–8[10.1109/ieeeconf58110.2023.10520480]
  116. 116.Jiaxue Han, Zhou Fu, Rui Gao, Cheng Chen, Guangjian Liu, Jianjie Zhang, Min Li. (2026). CAFF-Net: A multimodal fusion network based on diffusion enhancement and cross-modal feature modulation. , 789–794[10.1109/cnml68938.2026.11453228]
  117. 117.C. Kalimuthu, S. Muthukumaran, V. Angel Petricia. (2025). Efficient Channel Attention Based Convolution Neural Network for Skin Disease Classification. IFIP advances in information and communication technology, 361–370[10.1007/978-3-031-98356-6_27]
  118. 118.(2023). SKIN DISEASE DETECTION. International Journal of Progressive Research in Engineering Management and Science[10.58257/ijprems31453]
  119. 119.Raji Pandurangan, Buvaneswari Periyannan, Vadlamudi Sai GuruKrishna, Vinay Kumar Golla, Kalamakuntla Bhavani. (2025). A robust image-driven CNN algorithm to detect skin disease. AIP conference proceedings, 3159, 020036[10.1063/5.0247403]
  120. 120.Rupali S. Awhad, Dinesh M. Barode, Seema S. Kawathekar. (2023). Classification & Detection of Melanoma Skin Cancer in Early Stages Using CNN. International Journal for Research in Applied Science and Engineering Technology, 11(8), 194–199[10.22214/ijraset.2023.55143]
  121. 121.Dhyani Dhaval Desai, Jishnu Saurav Mittapalli, Karmanya Tyagi. (2023). Deep Neural Decision Forests for Accurate ESD Prediction: A Novel Approach. , 24, 1194–1201[10.1109/icacrs58579.2023.10404925]
  122. 122.Ashvini V. Ubale, Prashant L. Paikrao. (2020). Review of Skin Diseases Classification Methods Using Different Color Phase Models. Lecture notes in electrical engineering, 857–863[10.1007/978-981-15-1420-3_92]
  123. 123.Hakan Yılmaz, Zeynep Nida Can, Hatice Şevval Baki, Tahsin Çökmez, Mehmet Özdem. (2025). ScabAI: A Deep Learning-Based Mobile Application for Scabies Detection from Skin Images. Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, 13(4), 1371–1383[10.29109/gujsc.1601385]