Segmented Medical Image Classification with Deep Convolutional Neural Networks Architectures for WBC Detection

Katarzyna Wiltos, Agnieszka Polowczyk, Alicja Polowczyk, Marcin Wozniak, Michal Wieczorek

Abstract: Automated medical image classification is essential to improve diagnostic precision, reduce the burden on clinicians, and accelerate disease detection. This study evaluates the performance of various convolutional neural network (CNN) architectures in small-scale segmented medical image datasets. Models were trained from scratch without pre-trained weights, using deterministic augmentation pipelines to ensure reproducibility. Xception achieved the highest accuracy of 96.73%, showcasing the strength of advanced architectures in medical imaging. ResNet-50 and VGG16 also performed well, with accuracy of 95% and 94.52%, respectively, demonstrating their effective balance of depth and feature extraction for this task. This paper systematically evaluates the performance of various deep learning models, offering a comparative analysis of their strengths and limitations when applied to a segmented medical image dataset.