Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan

The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease's prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the preval...

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Published in:	Computer methods in biomechanics and biomedical engineering. Vol. 12; no. 1
Main Authors:	Singh, Harendra, Rana, Arun Kumar, Giri, Jayant, Shah, Mohd Asif, Mallik, Saurav, Sathish, T.
Format:	Journal Article
Language:	English
Published:	Taylor & Francis 31.12.2024 Taylor & Francis Group
Subjects:	Breast cancer ensemble-learning feature extraction frog leap algorithm Optimized k-means clustering
ISSN:	2168-1163, 2168-1171
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Abstract	The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease's prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.
AbstractList	The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease's prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.
Author	Shah, Mohd Asif Rana, Arun Kumar Sathish, T. Giri, Jayant Singh, Harendra Mallik, Saurav
Author_xml	– sequence: 1 givenname: Harendra surname: Singh fullname: Singh, Harendra organization: G.L. Bajaj Institute of Technology and Management – sequence: 2 givenname: Arun Kumar surname: Rana fullname: Rana, Arun Kumar organization: Galgotias College of Engineering of Technology – sequence: 3 givenname: Jayant surname: Giri fullname: Giri, Jayant email: jayantpgiri@gmail.com organization: Yeshwantrao Chavan College of Engineering – sequence: 4 givenname: Mohd Asif surname: Shah fullname: Shah, Mohd Asif email: drmohdasifshah@kdu.edu.et organization: Kabridahar University – sequence: 5 givenname: Saurav surname: Mallik fullname: Mallik, Saurav organization: Harvard T H Chan School of Public Health – sequence: 6 givenname: T. surname: Sathish fullname: Sathish, T. organization: Saveetha School of Engineering
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Snippet	The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease's prevalence... The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence...
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SubjectTerms	Breast cancer ensemble-learning feature extraction frog leap algorithm Optimized k-means clustering
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Title	Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan
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