DEEP LEARNING BASED SKIN CANCER DETECTION SYSTEM INTEGRATED WITH IoT AND RASPBERRY PI

ABSTRACT: Skin cancer is one of the most common forms of cancer worldwide, and early detection plays a vital role in improving treatment outcomes and saving lives. However, access to timely and accurate diagnosis can be limited, especially in remote or resource-constrained regions. In response to this challenge, this project presents the development of an intelligent skin cancer detection system powered by Raspberry Pi. The aim is to build a low-cost, portable, and efficient solution that leverages the power of deep learning and IoT to assist in the preliminary screening of skin lesions. Rather than relying on expensive diagnostic equipment or manual interpretation, the system processes pre-acquired images of skin lesions using advanced image processing techniques to enhance quality and isolate regions of interest. These refined images are then classified using a trained Convolutional Neural Network (CNN) model capable of distinguishing between benign, malignant, and potentially precancerous lesions with significant accuracy. The outcome of the diagnosis is displayed locally on a 16x2 LCD screen, while the data is also transmitted to a connected Android-based IoT application, enabling remote access for patients or healthcare professionals. Diagnostic records are saved in structured formats for future analysis, review, or integration into larger medical systems. By integrating deep learning with embedded systems and IoT technology, this project highlights a step toward decentralized, intelligent, and accessible skin cancer screening offering potential to bridge the gap in healthcare delivery, especially in underserved communities. KEYWORDS: Raspberry Pi, Skin Cancer Detection, Internet of Things, Convolutional Neural Network, Smart Healthcare, LCD Display Interface, Medical IoT Application, Dermoscopy Images.