An Ensemble Approach for Classification of Breast Histopathology Images

Dhivya, P.; Vasuki, S.

doi:10.1080/03772063.2019.1644974

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…EMS‐Net allows to extract features using multiple pre‐trained CNN models at multi‐scale and select the optimal subset of the fine‐tuned deep models. Likewise, the work conducted in (Dhivya & Vasuki, 2019) presents an approach for identification and classification of tumor in breast histopathology image based on ensemble classification of pre‐trained deep CNN architectures (LeNet, AlexNet and VGGNet‐16). The construction of classifier model is done by fine‐tuning the pre‐trained weights of these models separately.…”

Section: Histopathology Image Analysismentioning

confidence: 99%

A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

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Section: Histopathology Image Analysismentioning

confidence: 99%

A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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“…From ensemble model strong features are extracted and feature vector optimization done and classified using NN. This evaluation is mainly used to evaluate the benign and malignant tumour from mammographic imagees (Dhivya & Vasuki, 2019; Nagarajan, Muthukumaran, Murugesan, Joseph, & Munirathanam, 2021). But training of the CNN is not a trivial process which can take several hours or even days.…”

Section: Introductionmentioning

confidence: 99%

Detecting breast cancer using novel mask R‐CNN techniques

Jahangeer

Thambidurai

2022

Expert Systems

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Breast cancer is more common in women and the mortality rate also increases in recent days. Early detection of breast cancer is reduces the severity of the disease to some extent. Various image processing and classification techniques imposed on a particular image to detect and diagnose the breast cancer clearly. For early detection, an efficient methodology is needed. To reduce death rate, accurate discovery of the disease should be efficiently implemented. The mask R-CNN is used for segmentation to identify abnormalities and ensemble CNN is used to classify the benign and malignant tumour from the given mammographic breast cancer image. The input is fed into the enhanced fuzzy based median filter which is used to remove speckle noises which in turn increases the clarity of images. The noise-free images are fed into the proposed mask R-CNN for segmentation. It is an effective framework for segmentation of medical images. The classification process is then given into ensemble based CNN classifier for prediction of mammogram images. The ensemble based CNN is proposed for the classification of mammograms as benign and malignant. The proposed method focus on the optimization which is performed to minimize the memory requirements and running time while maintaining the high performance of the classifier. The performance metrics such as accuracy, precision, f-measure, and recall is evaluated to measure the efficacy of the proposed design. Using MIAS and DDSM dataset the performance metrics has been evaluated and compared with existing approaches. The results shows that the performance of the proposed method shows higher efficiency.

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