Early Detection of Various Types of Skin Cancer Using Deep Learning CNN – Vidit Goyal


Vidit Goyal

Vidit Goyal earned his Bachelors degree in Computer Science Engineering from Dr. A.P.J Abdul Kalam Technical University, India. He is also studying Bioinformatics and Artificial Intelligence from various MOOCs. His thesis focused on Early Detection of Skin Cancers Using Deep Learning CNN. He is currently working on developing vaccine using openAI under the AI firm Montreal.AI.


Over the last few years, there has been a rise in the reports of skin cancer in Asian continents. Regular skin checkups are recommended by dermatologist to identify the skin cancer in their initial stages. Hence, to assist this process, we proposed a mobile application that can detect the position of cancer and also classify into three categories such as Melanoma, Dermatofibroma, and Benign Keratosis lesions. We proposed a convolutional neural network and implemented two models – Modified Inception model and Modified Google’s MobileNet with transfer learning. The evaluation of the proposed method is done using HAM10000 dataset which is a collection of multi-source dermatoscopic images of common pigmented skin lesions. The experimental results shows that modified inception model performs better than Google’s MobileNet. The objective is to develop a commercial mobile application to detect the chances of early cancer so that a proper treatment can be suggested to the patient.

Date: December 5th, 2020 – 6:00 pm (GMT+3)

Language: English

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