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Title: :  SKIN CANCER DETECTION USING DEEP LEARNING
PaperId: :  17449
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-17449
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Austin Joseph MAnand Institute of higher Technology
Chubaas Hari Manikandesh GAnand Institute of higher Technology
Malathi AAnand Institute of higher Technology
Balaji A SAnand Institute of higher Technology

Abstract

Computer Science and Engineering
HAM10000, Skin cancer, types of cancer, CNN, RESNET-50, SMOTE, Sampling, Deep learning .
Human skin is the most exposed part of the human body which needs to be prevented from heat, light, dust and direct exposure to UV rays. Skin cancer is one of the dangerous diseases found recent days. The higher damage caused by the skin cancer is mostly due to the late identification of it. Some of the challenges that affects the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labelled data and imbalance of skin cancer datasets. This content provides a data augmentation technique based on Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance problem in the given images. Then it is a challenging task to distinguish between malignant and benign skin lesions as they are alike in their physical appearances. This results in more unnecessary biopsies. To tackle this problem, we developed an enhanced image classification model which can act as a preliminary check before moving to a costlier biopsy. The proposed model can recognize 7 distinct types of skin lesions. Analyses have been performed on the HAM10000 dataset. The classification process is based on transfer learning using multiple pre-trained models, combined with class-weighted loss and augmentation of the data. Experimental analysis shows that the modified ResNet50 model is capable of identifying skin lesion images into one of the seven classes of image.

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IJARIIE Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. "SKIN CANCER DETECTION USING DEEP LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 3 2022 Page 4640-4646
MLA Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. "SKIN CANCER DETECTION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 8.3(2022) : 4640-4646.
APA Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, & Balaji A S. (2022). SKIN CANCER DETECTION USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 8(3), 4640-4646.
Chicago Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. "SKIN CANCER DETECTION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 3 (2022) : 4640-4646.
Oxford Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. 'SKIN CANCER DETECTION USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, 2022, p. 4640-4646. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/SKIN_CANCER_DETECTION_USING_DEEP_LEARNING_ijariie17449.pdf (Accessed : ).
Harvard Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. (2022) 'SKIN CANCER DETECTION USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 8(3), pp. 4640-4646IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/SKIN_CANCER_DETECTION_USING_DEEP_LEARNING_ijariie17449.pdf (Accessed : )
IEEE Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S, "SKIN CANCER DETECTION USING DEEP LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, pp. 4640-4646, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/SKIN_CANCER_DETECTION_USING_DEEP_LEARNING_ijariie17449.pdf [Accessed : ].
Turabian Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. "SKIN CANCER DETECTION USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 3 ().
Vancouver Austin Joseph M, Chubaas Hari Manikandesh G, Malathi A, and Balaji A S. SKIN CANCER DETECTION USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : ]; 8(3) : 4640-4646. Available from: https://ijariie.com/AdminUploadPdf/SKIN_CANCER_DETECTION_USING_DEEP_LEARNING_ijariie17449.pdf
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