International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.12, No.5, Oct. 2022

Diagnosis of Skin Cancer Using Machine Learning and Image Processing Techniques

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Prashant Kaler, Shilpa Kodli, Sudhir Anakal

Index Terms

Dermoscopic Images, Machine Learning, Convolutional Neural Networks, Skin Lesion, ResNet-50.


Skin Lesion is a part of the skin that can be caused by abnormal growth in the epithelium layer on the skin. There are nine types of skin lesion like Actinic Keratoses (AK), Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevi (MV), Benign Keratosis (BK), Vascular Lesions (VASC), Squamous Cell Carcinoma (SCC), and Pigmented Benign Keratosis (PBK). The aim of this study is to spotlight on the problem of skin lesion classification based on early detection of the disease using deep learning techniques. This approach is used to work out the problem of classifying a dermoscopic image. The dermoscopic is a digital device; in this case Smartphone is attached to a lens and collects the images through the device. The proposed spotlight is built in the region of using Convolutional neural network architecture and ResNet-50 module is used to predict Skin-Lesion classification. The dataset used in this research was taken from kaggle repository. The proposed work uses ResNet-50 CNN model which has yielded 93% of accuracy for detecting Skin Cancer, previous work was carried out using Visual Geometry Group model which yielded 73% accuracy. In the proposed work we have considered 25,000 images of skin lesion. Hence we are able to attain this accuracy with more reliable Machine Learning algorithms compared to the previous work.

Cite This Paper

Prashant Kaler, Shilpa Kodli, Sudhir Anakal, "Diagnosis of Skin Cancer Using Machine Learning and Image Processing Techniques", International Journal of Education and Management Engineering (IJEME), Vol.12, No.5, pp. 38-45, 2022. DOI:10.5815/ijeme.2022.05.05


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