Diagnosis of Skin Diseases using Deep Convolution Neural Network

Skin problems are rising. Advanced diagnosis methods are needed. Convolutional Neural Networks (CNNs) show their usefulness in dermatology. The study examines how classical CNNs, DenseNet and MobileNet perform at skin disease detection with respect to functionality and performance benchmarks. DenseNet espouses the notion of dense connectivity to facilitate feature reuse, in order to maximize classification accuracy while minimizing the vanishing gradient problem, thus yielding greater accuracy in dermatological image classification. Research has shown DenseNet has superior performance in skin lesion identification, achieving detection accuracy well above 70%. MobileNet focuses on mobile phones and edge computing, utilizing depthwise separable convolutions, requiring simplified adaptations of conventional CNNs process, without loss of performance, to reduce processing parameters compared to dense CNNs, yielding detection accuracy performance greater than 90%, in skin diseases classification tasks. Classical CNNs are simplified adaptations of classical CNNs that are relatively easier to use and require less processing time, but yield relatively poor performance on the more complex dataset, than DenseNet and MobileNet. Overall, DenseNet and MobileNet architectures enhanced diagnostic accuracy and speed for skin disorders will improve patient outcomes through bessere disease identification, with earlier intervention possibilities. With the advancement machine learning approaches are more likely to implement advanced CNN networks, such as DenseNet and MobileNet, in practice where clinicians can utilize performance benefits of advanced CNNs in dermatology practices to improve patient prognoses, and clinical practice will be shifted towards more proactive healthcare topics..