Skin cancer represents a significant global health concern, with early diagnosis being crucial for effective management and treatment outcomes. This project introduces "SpotVision.AI - Skin Cancer Detection" a system that utilizes image processing and deep learning techniques to identify and classify various types of skin cancers accurately while keeping it deployable. At the core of this system are Convolutional Neural Networks (CNNs), particularly the Inception V3 model, known for its efficiency in processing complex image data. The system is meticulously trained on a comprehensive dataset of dermoscopic images to detect key skin cancer types such as melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). This approach has demonstrated promising results in improving the early detection capabilities, significantly contributing to potential reductions in mortality rates. Furthermore, "SpotVision.AI" features a user-friendly interface that allows for easy image upload and instant display of diagnostic results, enhancing the accessibility and efficiency of skin cancer screenings. Skin cancer is one of the most prevalent cancers globally, and its early detection is crucial for effective treatment and patient survival. This research explores the application of machine learning algorithms for predicting skin cancer using image-based data. Leveraging the power of convolutional neural networks (CNNs) and traditional classifiers like Support Vector Machines (SVM), the study aims to classify skin lesions into benign and malignant categories. The model is trained and validated using the ISIC dataset, and its performance is evaluated based on accuracy, precision, recall, and F1-score. The results demonstrate the efficacy of machine learning in aiding dermatological diagnoses, potentially enabling faster and more accurate detection of skin cancer.
Convolutional Neural Networks (CNNs), SVM, Receiver Operating Characteristic Curve (AUC-ROC).
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