Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

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Structure of Skin
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Published: Mar 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6158
Keywords:
Skin lesions, convolutional neural networks, computer vision machine learning, deep learning

Main Article Content

Venkata Rao Yanamadni
Assistant Professor, KG Reddy College of Engineering and Technology, Department of Computer Science and Engineering, Chilkur Village, Moinabad Mandal, Ranga Reddy District, Telangana -501504, India
J. Seetha
Associate professor, Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai
T. Sathish Kumar
Associate Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad
Sathish Kumar Kannaiah
Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh – 522502
Balajee J
Associate professor, Department of Computer Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh
Madamanchi Brahmaiah
Assistant Professor, Department of CSE, RVR & JC college of engineering (Autonomous), Chowdavaram, Guntur, Andhra Pradesh

Abstract

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

Article Details

How to Cite
Yanamadni, V. R. ., Seetha, J. ., Kumar, T. S. ., Kannaiah, S. K. ., J, B. ., & Brahmaiah, M. . (2023). Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 293–302. https://doi.org/10.17762/ijritcc.v11i2s.6158
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Vol. 11 No. 2s (2023)
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Articles

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