Perbandingan Performa Arsitektur CNN Terhadap Klasifikasi Tumor Otak Menggunakan Data MRI - Saputri, Sekar Dewi Harnum; Lukman, Achmad; Irsan, Muhamad (Forum Kerjasama Pendidikan Tinggi, 2025)
This study discusses the performance comparison of four Convolutional Neural Network (CNN) architectures in brain tumor classification using histopathology images. CNN has proven its effectiveness in improving the accuracy and efficiency of image-based medical diagnosis. This study compares four popular architectures, namely ResNet, AlexNet, InceptionNet, and VGG12, using a histopathology image dataset with a total of 2,145 images divided into training (70%), validation (15%), and testing (15%) subsets. The results show that the VGG12 model achieves the best accuracy of 98.0%, followed by InceptionNet with an accuracy of 97.3%. The ResNet model achieves an accuracy of 94.3%, while AlexNet has an accuracy of 93.2%. In addition, the VGG12 model shows consistent performance with high precision, recall, and F1-Score values, making it a superior choice for medical applications. This study provides in-depth insights into the advantages and limitations of each CNN architecture, as well as implementation guidelines to support the development of image-based medical diagnosis applications efficiently and accurately.