Improving Skin Cancer Detection with Machine Learning Solutions

Abstract

Introduction: With over 5,000,000 new cases reported each year in the US, skin cancer is a serious public health issue. Significant hazards are associated with melanoma, the most deadly kind, as well as non-melanoma malignancies including basal cell carcinoma and squamous cell carcinoma. Over 95% of people survive when they are detected early. This undertaking aims to create a Convolutional Neural Network (CNN) model for the early diagnosis of skin cancer. The CNN model integrates a variety of network designs, such as convolutional, dropout, pooling, and dense layers, using Python with Keras and Tensor Flow. By accelerating convergence, transfer learning approaches improve the model's adaptability to different datasets and clinical contexts. With the use of state-of-the-art technology, our initiative seeks to transform the detection of skin cancer and give medical professionals a useful tool for saving lives everywhere.

Objectives: Our goal is to leverage various network architectures and ISIC dataset to enhance early detection and save lives. Skin cancer, primarily driven by melanoma, presents a growing global health challenge. Timely detection is paramount for improving outcomes.

Methods: To make a system for detecting skin issues like cancer a diverse collection of images depicting various skin conditions is gathered, ensuring representation across different demographics. These images undergo cleaning and standardization processes to ensure consistency in quality and format. Subsequently, important features relevant to spotting skin problems are extracted from these images, such as texture, color, and shape. Using sophisticated techniques like Convolutional Neural Networks (CNNs).

Results: The research work for development and implementation of the "Skin cancer and Diseases detection using ML" project have yielded significant outcomes, marking a transformative advancement in diagnostics. The ML dermatological algorithms yielded significant results, showcasing exceptional accuracy in identifying various skin cancers and diseases.

Conclusions: The Skin Diseases and Cancer Detection system offers significant benefits, including early detection of potentially harmful conditions, streamlined diagnosis processes, and improved patient outcomes. By leveraging advanced technologies such as artificial intelligence and image recognition, this system enhances accuracy and efficiency in identifying various skin diseases and cancerous lesions