• Mohammed Maaz
• Mohammed Adnan
• Mudassir Ahmed
• Samarth AM
• Usman AIjaz

Skin cancer is one of the deadliest types of cancer. If it is not diagnosed and treated early on, it is likely to spread to other areas of the body. It is primarily caused by abnormal skin cell development, which occurs often when the body is exposed to sunlight. The Surveillance Furthermore, identifying skin malignant development in its early stages is an expensive and difficult process. It is graded according to where it grows and what type of cell it is. The classification of lesions necessitates a high level of precision and recall. The MNIST HAM-10000 dataset containing dermoscopy images will be included in this article. The aim is to propose a method that uses a Convolution Neural Network to diagnose skin cancer and classify it into various groups. Image recognition and a deep learning algorithm are used in the diagnosis process. The noise and picture resolution were removed from the dermoscopy shot of skin cancer that was taken. Using different image augmentation methods, the image count may also be improved. Finally, the Transfer Learning approach is used to improve the image recognition accuracy even further. The weighted average Precision of our CNN model was 0.88, the weighted average Recall was 0.74, and the weighted f1-score was 0.77. The accuracy of the transfer learning method using the ResNet model was 90.51 percent.

Skin cancer, dermoscopy

"Detection of Non-Melanoma Skin Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c727-c731, July-2022, Available :http://www.jetir.org/papers/JETIR2207291.pdf

"Detection of Non-Melanoma Skin Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc727-c731, July-2022, Available at : http://www.jetir.org/papers/JETIR2207291.pdf