An Improved Transfer Learning Approach for Classification of Types of Cancer

Abstract

Melanoma mortality rate is very high and is one kind of skin cancer. So, it is essential to identify skin cancer at an initial phase hence we can minimize the mortality rate, but sometimes recognition of the skin lesion type is very difficult due to its similarity leads to wrong treatment. Hence it is required to classify the skin lesion accurately at an initial phase for medicating a patient accurately and to save their lives. Here we proposed a framework for a very precise skin lesions classification. This uses transfer learning along with a pre-trained model and MobileNet. By using our proposed system, we can categorize the different skin lesion types accurately. Lesions are divided into eight types including melanoma, benign keratosis, basal cell carcinoma, actinic keratosis, melanocytic nevus, vascular lesion, dermatofibroma, and squamous cell carcinoma. The dataset used is ISIC 2019 challenge dataset to perform experiment on types of skin lesions. If the input image is not classified in any one of the eight types, then that image is classified as an unknown image. Hence, according to experiment our proposed system able to find the lesion type very accurately and will help to dermatologist to do accurate treatment.