Benign-Malignant Breast Histology Classification using MobileNet Variants: An Analysis

Automatic analysis of the medical data is one of the common practices followed to detect diseases with better accuracy. Deep Learning (DL) tool-based medical image examination is one of the approved clinical practices, and the outcome of this process supports the treatment planning and execution. This work proposes a DL tool based on the ConvNeXt (CN) scheme to classify the chosen Breast Histology Images (BHI) into benign and malignant classes. The various phases of the proposed DL-tool include: image collection from the database and resizing it to 224x224x3 pixels, feature extraction using the chosen CN-model, feature reduction using 50% dropout, and serial features fusion to get fused- features-vector (FFV), and binary classification with 5-fold cross-validation. The merit of the developed scheme is confirmed using the classification executed with the chosen CN-feature and the FFV. The outcome of this study confirms that the FFV-based classification provides a detection result upto 99% when the SoftMax-based classification is executed. This confirms that the proposed DL-tool provides a better result on the chosen image database.