AI-Powered Dermatology: Achieving Dermatologist-Grade Skin Cancer Classification
Priyanka Kaushik, Yash Chopra, Amar Kajla, Minakshi Poonia, Akram Khan, Dhruv Yadav
Abstract
In the realm of dermatology, the accurate diagnosis of skin cancer has long been a challenging endeavor. This paper introduces a cutting-edge solution for achieving dermatologist-grade skin cancer classification through the power of artificial intelligence (AI). Departing from traditional methods that necessitate laborious manual feature extraction and domain-specific preprocessing, our system adopts a deep neural network architecture, specifically Google Net Inception v3 CNN, fine-tuned using a vast and diverse clinical image dataset. Dataset comprises 135,550 images meticulously organized within a structured taxonomy encompassing 2,055 distinct disease categories. To unlock the full potential of fine-grained classification, An innovative algorithm is introduced to facilitate precise identification of various skin diseases. This research underscores the transformative potential of AI-powered dermatology in the realm of early skin cancer detection. By achieving dermatologist-level accuracy, this approach has the capacity to significantly impact public health outcomes, particularly in regions where skin cancer is a prevalent concern.