Modification of Convolutional Neural Network GoogLeNet Architecture with Dull Razor Filtering for Classifying Skin Cancer
Abstract
Skin is the widest external organ covering the human body. Due to a high intensity exposure to the environment, the skin can experience various health problems, one of which is skin cancer. Early detection is needed so that further medication for patients can be done immediately. In this regard, the use of artificial intelligence (AI)-based solutions in detecting skin cancer images can be used to detect skin cancer potentials. In this study, the classification of benign and malignant skin cancer types was carried out by utilizing GoogLeNet Convolutional Neural Network (CNN) method. The GoogLeNet architecture has the advantage of having an inception module, allowing the convolution and pooling processes to run in parallel terms that can reduce computing time and speed up the classification process without lowering the system accuracy. This study consisted of several stages, starting from the data acquisition of 600 skin cancer images from Kaggle.com to the uniformity of the input size that allows the system to work faster. There was also a utilization of dull razor filtering to reduce input image noise due to hair growing along the epidermis. After the preprocessing process was complete, GoogLeNet architecture processed the image input before categorizing the input into benign or malignant skin cancer. The system’s performance was then evaluated using performance parameters such as accuracy, precision, recall, and F-1 score, and it was compared to other methods. The system managed to obtain satisfactory results, including the accuracy of 97.73% and the loss of 1.7063. As for precision, recall, and F-1 score parameters, each received an average value of 0.98. The system performance proposed by the authors successfully have better accuracy compared to the previous study with much less use of datasets. The test results show that CNN method is able to detect and classify skin cancer accurately, so it is expected that this method could help medical workers in diagnosing the community.
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