Hybrid Deep Learning Approach for Diagnosing Papulosquamous Diseases using Convolutional Neural Networks and Attention Mechanisms

Psoriasis and eczema are examples of papulosquamous illnesses that provide substantial diagnostic problems because of their clinical similarity and presentational diversity. Conventional diagnostic techniques take a lot of time and primarily depend on specialized knowledge. Convolutional Neural Networks (CNNs), a recent development in deep learning (DL) have shown impressive results in medical image interpretation. A new hybrid deep learning (DL) model for diagnosing papulosquamous disorders (PSDs) has been proposed to increase the accuracy of prediction. The suggested model combines metadata-based analysis (such as demographic and clinical data), image-based feature extraction using Convolutional Neural Networks, or CNNs and sophisticated attention mechanisms to highlight patterns unique to a given disease. The suggested model is analyzed on a sizable dataset of dermatological image labels, demonstrating better performance than conventional CNN models in terms of overall diagnosis accuracy, precision, recall and f1-score. This hybrid model may help physicians make more certain diagnoses of papulosquamous disorders, which would benefit patients and enable rapid treatments.