Authors - Saurabh Nimje, Reena Satpute, Utkarsha Pacharaney, Anup Bhitre Abstract - Breast cancer is considered as one of the top causes of mortality on women across the world making early and accurate diagnosis a key element in addressing patient outcomes. The work introduces artificial breast instances of cancer detection techniques in ultrasound imaging by means of Contrast Limited Adaptive Histogram Equalization (CLAHE) and ensemble deep learning framework. Data used was a balanced data set comprising of 200 ultrasound images that are made to be benign, malignant, and normal. The CLAHE preprocessing was quite useful in terms of image quality as it provided edge and local contrast enhancement and profited letting the lesions be seen more effectively. A number of the convolutional neural network (CNN) architectures were tuned collectively in an ensemble arrangement with soft voting and weighted averaging, and this produced an improved classification performance. The proposed model returned an accuracy of 93.7%, sensitivity of 92.5%, specificity of 94.5% and AUC of 0.97 even better than the baseline general CNN models and the single CNN models with CLAHE. The findings are indicative of the fact that CLAHE-enhanced ensemble learning is a robust, reproducible, and promising tool in breast cancer detection within ultrasound imaging that holds a great promise in clinical.
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