Multimodal CNN-Based Framework for Enhanced Detection and Classification of Skin Cancer and Cysts

The early and precise detection and classification of skin cancers and skin cystic lesions remain extremely important in the practice of dermatology. In this paper, we present a novel multimodal CNN framework that aims to improve diagnostic performance by fusing different types of data such as dermoscopic images and histopathological slides along with clinical information and optional genomic information. The method innovatively includes several novel steps: 1. Advanced Feature Extraction Networks: Uses dedicated CNN models such as U-Net for image segmentation and ResNet for feature extraction for dermoscopy and histopathology images. 2. Innovative Feature Fusion Networks: Leverages the techniques of cross-modal attention mechanisms to integrate features from different modalities to improve the prediction performance of the model. 3. Multi-Input CNN Architecture: Processes the clinical records through its network to obtain embeddings which are fused with the image features to examine the lesion along with the patient’s data. 4. Unified Detection and Classification Model: It utilizes a multi-task learning strategy that employs attention layers for important image sections and ensemble learning to integrate forecasts from different CNN models as it does joint lesion detection with simultaneous classification. The evaluation across different datasets indicates better accuracy, sensitivity and specificity than all previous methodologies making it more applicable in real hospitals. In doing so, it tackles difficult data fusion and computation problems thus greatly improving skin cancer as well as cystic lesion detection-classification hence enhancing both clinical procedure and patient outcome.