DEEP LEARNING MODEL FOR SKIN CANCER DETECTION AND MULTI CLASS CLASSIFICATION FOR ADVANCEMENT IN
Keywords:
Skin Cancer, Deep Learning, Classification, Diagnosis, HealthcareAbstract
Skin cancer is a prevalent and potentially life-threatening disease, making early and accurate diagnosis crucial for successful treatment. In this study, we present a deep learning model for the detection and multiclass classification of skin cancer lesions. Skin cancer is a global health concern, and the conventional systems for diagnosis heavily rely on the expertise of dermatologists, leading to limited accessibility, subjectivity, and delays in diagnosis. These drawbacks can result in missed opportunities for early intervention. To address these issues, we propose a deep learning-based system to automate the detection and classification of skin cancer lesions into multiple categories, including melanoma, basal cell carcinoma, and squamous cell carcinoma. Our proposed system not only overcomes the limitations of the conventional methods but also offers scalability, consistency, and the potential for wider accessibility, thereby improving the accuracy and timeliness of skin cancer diagnosis. We evaluate the model on a comprehensive dataset, demonstrating its effectiveness in aiding dermatologists and healthcare professionals in the early diagnosis and treatment of skin cancer, ultimately contributing to improved patient outcomes and healthcare efficiency.
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