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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science
Sinaga, Jesica Trivena
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Computer Science & IT

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Klasifikasi Kanker Kulit menggunakan Convolutional Neural Network dengan Optimasi Arsitektur Sinaga, Jesica Trivena; Faudyta, Haniifa Aliila; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6141

Abstract

Skin cancer is a severe condition characterized by the abnormal growth of skin cells, often triggered by ultraviolet exposure and genetic factors. Early detection of skin cancer is essential for improving patient recovery rates, given the high incidence and significant impact of the disease. This study aims to develop a skin cancer classification system using the Convolutional Neural Network (CNN) method with the VGG-16 architecture, known for its effectiveness in medical image analysis. The CNN method was chosen because it can extract complex features from images. At the same time, the VGG-16 architecture was selected for its depth and ability to capture fine details in images—critical for distinguishing between types of skin cancer. The dataset was sourced from the ISIC platform and optimized through data augmentation techniques to address data imbalance issues. The research results indicate that while a basic CNN can provide good accuracy, implementing the VGG-16 architecture significantly increases accuracy. The basic CNN model achieved a training accuracy of 95.68% and a validation accuracy of 89.83%, whereas the CNN with VGG-16 reached a training accuracy of 96.21% and a validation accuracy of 90.89%. These findings suggest that combining CNN with VGG-16 effectively detects skin cancer, with VGG-16 providing a slight accuracy improvement, highlighting this architecture's potential as a more accurate tool to support skin cancer diagnosis.
Implementation of MobileNet Architecture for Skin Cancer Disease Classification Faudyta, Haniifa Aliila; Sinaga, Jesica Trivena; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8771

Abstract

As the number of occurrences of skin cancer increases year, it becomes more and more crucial to identify the disease accurately and effectively. This study aims to implement and evaluate the MobileNet architecture for classifying nine types of skin lesions using the ISIC 2020 dataset and to compare MobileNet's performance with other CNN architectures, such as VGG-16 and LeNet, in terms of accuracy and computational efficiency. The study also investigates the impact of image preprocessing on model accuracy. The methodology comprises data collection, preprocessing, and model development, leveraging transfer learning from MobileNet pre-trained on ImageNet. Data preprocessing involves resizing images to 224 x 224 pixels and normalizing pixel values. To augment the dataset, techniques such as rotation, zooming, horizontal flipping, and brightness and contrast adjustment are applied. To address class imbalance, oversampling is used to obtain 500 images per class. The model architecture includes Global Average Pooling, a Dense layer with 1024 units and ReLU activation, and a Dropout layer with a 0.2 probability. Various training scenarios with batch sizes (8, 16, 32, 64) and learning rates (0.001, 0.0001) are evaluated, incorporating dropout and ReLU activations. The optimal performance was achieved with oversampling, dropout, and a learning rate of 0.0001, yielding a training accuracy of 99.64% and a validation accuracy of 86.89% after oversampling, resulting in 3,600 training and 900 validation images with an 80:20 data split. The results suggest overfitting due to dataset limitations. Future work should focus on fine-tuning and ensemble methods to improve validation performance.
Co-Authors Egia Rosi Subhiyakto, Egia Rosi Faudyta, Haniifa Aliila