DEEP ENSEMBLE METHODS FOR IDENTIFICATION OF MALICIOUS TISSUES IN NOISY BREAST HISTOPATHOLOGICAL IMAGES

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This work addresses the issues of noise and tissue appearance fluctuations in histopathology image classification by using a novel deep ensemble method. The experiment’s images were inherently noisy; however, the proposed approach includes features that allow for noise to be effectively encountered while classification tasks are being completed. This integration streamlines the categorization process by eliminating the requirement for a separate denoising phase. This approach encompasses studies on two types of noise, namely Gaussian and Rician, both commonly encountered in histopathological images. Remarkably, our proposed model demonstrated effectiveness in handling both types of noise, yielding satisfactory performance across diverse noise conditions. The proposed ensemble model achieves an accuracy of 83.74%, an [Formula: see text]1-score of 81.72%, an [Formula: see text]2-score of 81.04%, and an MCC of 83.99% for the highest level of rician noise. The proposed approach improves classification resilience and accuracy by combining the output of several deep-learning models. It does this by increasing the [Formula: see text]2-score for malignant classes by 3–5%, which helps to reduce False Negatives. This approach differs from current technology and has promising implications for the diagnosis and treatment of breast cancer. Compared to other approaches, our suggested model performs better at higher noise levels. LIME and saliency map integration improve the interpretability of model decisions, which in turn improves classification accuracy and decision clarity. These features emphasize the adaptability and resilience of the suggested method, highlighting it as a potential instrument for enhancing the results of breast cancer diagnosis and therapy in clinical settings. The workload for pathologists is lessened, and diagnostic consistency and accuracy are improved through automation of the classification process.