Abstract
Among several cancer types, most common cancer is breast cancer diagnosed in women and automatic classification of breast cancer images is a crucial task using computer-aided analysis. From the statistical analysis, it is observed that the rate of breast cancer is nearly 12% of all cancer types worldwide. Moreover, around 25% of women are affected with breast cancer. Hence, there is a high demand for rapid and appropriate analysis of breast cancer images. In the current situation, DL approaches are mostly preferred for this purpose. The most important concept to choose deep learning method for diagnosis of breast cancer medical images is that more accurate results can be quickly obtained compared to other conventional machine learning techniques. This research work comes up with an innovative deep learning move toward based on (CNN) integrated with encoder and UNet. Improved performance measure and classification accuracy rate was obtained for the proposed DT-KNN 5, RF-KNN 5, DT-KNN 6, RF-KNN 6 models. The overall structure of the EfficientNet model is an endurable architecture constructed with attention components. Every image is processed one by one with the use of augmentation methods before transferring it as an input to the EfficientNet model. With constant number of images and every image features are modified with augmentation methods like shift, flip, rotation, and brightness using ensemble classifiers such as K-Means Nearest Neighbor, Random Forest, and Decision Tree. This proposed model has produced better success rate than EfficientNet-B3, ResNet50, and DenseNet121 models for the same dataset.
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All the data are available in the manuscript. The experimental dataset description is explained in detail.
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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.
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Prasath Alias Surendhar, S., Kanna, R.K. & Indumathi, R. Ensemble Feature Extraction with Classification Integrated with Mask RCNN Architecture in Breast Cancer Detection Based on Deep Learning Techniques. SN COMPUT. SCI. 4, 618 (2023). https://doi.org/10.1007/s42979-023-01893-z
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DOI: https://doi.org/10.1007/s42979-023-01893-z