Abstract
Detection of early cancer greatly improves the results of treatment and the patient's survival percentage. The article presents a method to automatically classify cancer cells in histological images that is based on a convolutional neural network (CNN). A multi-level CNN architecture was proposed due to strong data growth and advanced pre-processing techniques, which could effectively detect micro-structural aspects in medical imaging data. The model achieves 94.6% accuracy when significant performance metrics, including accuracy, sensitivity, specificity, and F1-score, are used. These results show how models successfully eliminate manual interpretation errors, reduce clinical turnaround time, and can be integrated into real clinical systems. The study stands as a scalable and reliable method to diagnose early cancer in a clinical context.
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