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SETSCI - Volume 6(1) (2023)
BMYZ2023 - Cognitive Models and Artificial Intelligence Conference, Ankara, Türkiye, Oct 26, 2023

A New Convolutional Neural Network Model for Skin Cancer Detection in Dermatoscopic Images
Mustafa Furkan Keskenler1*, Deniz Dal2
1Ataturk University, Erzurum, Türkiye
2Ataturk University, Erzurum, Türkiye
* Corresponding author: mfkeskenler@atauni.edu.tr
Published Date: 2023-11-26   |   Page (s): 8-12   |    59     3
https://doi.org/10.36287/setsci.6.1.006
ABSTRACT Skin cancer is the abnormal growth of skin cells and one of the most common cancers of all. There are several types of skin cancer. Melanoma, a form of skin cancer, has increased 237% in Turkey in the last 30 years. While the U.S. adds about one million new cases of melanoma each year, this rate in Turkey is 1.9 per 100 thousand men and 1.3 per 100 thousand women. The death rate from skin cancer is 1 in 100 patients worldwide. As with all cancers, early diagnosis of this cancer is crucial, and artificial intelligence (AI) appears to be a promising technology for detecting early-stage skin cancer from dermoscopic images in recent years. AI-based studies for skin cancer classification are usually performed using three different types of images:
dermoscopic images, clinical images, and histopathological images. In this study, a new deep learning model called CNN-BM (Convolutional Neural Network-Based Model) is proposed for skin cancer diagnosis using dermoscopic images. In this context, HAM10000, a commonly employed public dataset consisting of 10015 dermoscopic images, is utilized. The proposed model not only increases the success rate in the training process, but also reduces the execution time. CNN-BM consists of convolution, max pooling, dropout, flatten, and activation layers. Relu and sigmoid functions are chosen as activation functions. CNNs are sensitive to the batch size values, which significantly affects the quality of the model. Unlike other deep learning models used in the literature for skin cancer diagnosis, the proposed model uses a small batch size to prevent overfitting and increase the regularization effect. Similarly, by incorporating a dropout layer and dense-sparse-dense training techniques into the model, overfitting is avoided and the success rate of the network is increased. To determine the most efficient values for the hyperparameters, a trial-and-error method is employed. Research findings indicate that the success of the model is superior to other studies in the literature with 86.48% accuracy and 85.13% precision rates.
KEYWORDS skin cancer, cancer detection, CNN, CNN-BM, deep learning
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