Comparative Analysis of Deep Learning Models for Early Prediction and Subtype Classification of Ovarian Cancer: A Comprehensive Study
Keywords:
AlexNet, Convolution neural network, Ovarian cancer, VGGNetAbstract
This study compares the performance of two state-of-the-art deep convolutional Neural Network architectures, AlexNet and VGG-19, for predicting and classifying the sub-types of ovarian cancer from histopathological images. The dataset consisted of 500 images, augmented to generate 24,742 images, which were used to train both models. The results showed that VGG-19 outperformed over AlexNet, achieving an accuracy of 90% compared to 70% for AlexNet. Other performance metrics, such as precision, recall, F1-score, and AUC-ROC, were also analyzed. This study provides valuable insights into the use of computer-aided diagnosis for accurately Predicting the diagnosis and subtype of ovarian cancer, which can lead to early detection and treatment.
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