Abstract
The area of medical image analysis has genuine obstacles, such as small data sets, complicated fine-tuning, and choosing the right architecture. This paper proposes a CancerVisionNet model based on the convolutional neural network (CNN) architecture with many layers to extract and classify features from cancer images. To train the CancerVisionNet model and avoid overfitting, data augmentation is carried out using a data set consisting of 220,025 images. The proposed CancerVisionNet model is evaluated on the PatchCamelyon dataset. Its remarkable area under the receiver operating characteristic (ROC) curve (AUC) measure is about 0.9. Compared to the other studies, the CancerVisionNet model stands out with a higher accuracy (95.4%). Moreover, this work demonstrates the potential of CNNs in medical image analysis, providing an effective approach to enhance classification accuracy and paving the way for further advances in the field. Although the results of this study pertain to histopathology and the PatchCamelyon dataset, the potential for a broader application awaits cross-domain validation. Future research works can include exploring alternative architectures and scalability to larger datasets.
References
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