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Enhancing Skin Cancer Detection Through

Deep Learning Techniques

Prof. Sofiya Mujawar, Kumar Mayank, Aisha Singh, Kamiya Mujawar, Kalyani Hankare

sofiya.mujawar@dypatiluniversitypune.edu.in, mayankjaiswal1411@gmail.com, aishasingh17672@gmail.com, kamiyahm19@gmail.com, kalyanihankare1@gmail.com

B.Tech, Department of Computer Engineering, SOET

D Y Patil University, Ambi

Abstract– Skin cancer is a common and possibly deadly disease that affects the skin's outer layers. Promoting knowledge of skin cancer, its risk factors, and the need for early detection can help combat the illness and reduce its impact on individuals and communities around the world. In this project, we describe a novel use of deep learning techniques to detect skin cancer in its early stages using dermatoscopic images. The primary goal of this study is to create an accurate and reliable model for predicting various types of skin cancer, including actinic keratoses and intraepithelial carcinoma (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv), and pyogenic granulomas and hemorrhage (vasc). The project is written in Python, and the primary algorithm/model used is the Convolutional Neural Network (CNN) architecture. CNNs are ideal for image classification jobs because they can automatically learn relevant characteristics from input. Using CNNs, our model is trained on the HAM10000 (Human Against Machine with 10,000 training images) dataset, which contains 10015 high-resolution dermatoscopic images obtained from distinct populations and acquired via multiple modalities. The achieved findings illustrate the effectiveness of our proposed method. The model had an amazing training accuracy of 96.00% and a validation accuracy of 97.00%. These high accuracy rates demonstrate the promise of our deep learning-based skin cancer prediction system as a trustworthy tool for early diagnosis, allowing healthcare practitioners to make more informed decisions and improve patient outcomes. Our effort contributes to the field of dermatological research and machine learning, providing valuable insights into the use of deep learning algorithms for skin cancer prediction. Furthermore, the publicly available HAM10000 dataset, which includes a wide range of dermatoscopic pictures, can be a great resource for academic study and future advances in the field of medical image processing and classification.

Key Words– benign, CNN, early detection, skin cancer, machine learning