Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements
Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations i...
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Published in | International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 18 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Dordrecht
Springer Netherlands
22.01.2024
Springer |
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Abstract | Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions. |
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AbstractList | Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions. Abstract Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions. Abstract Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions. |
ArticleNumber | 14 |
Author | Gupta, Muskan R, Mahesh T Sinha, Deepak Kumar Thakur, Arastu Mishra, Kritika Kumari Guluwadi, Suresh Venkatesan, Vinoth Kumar |
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Cites_doi | 10.1109/TMTT.2022.3209679 10.3390/diagnostics13172746 10.1016/j.ins.2022.06.091 10.1007/s42979-023-01879-x 10.3233/JIFS-231480 10.59785/tjhest.v1i2.25 10.31557/APJCP.2023.24.2.531 10.1109/JBHI.2020.3003316 10.1007/978-981-19-7982-8_8 10.1108/IJICC-10-2019-0116 10.3390/app13010600 10.59785/tjhest.v1i2.24 10.1109/IC3S57698.2023.10169383 10.1109/Confluence56041.2023.10048862 10.1109/ICCCNT54827.2022.9984513 10.1109/SIU53274.2021.9477801 10.1109/ICSSES58299.2023.10199470 10.1109/ICIEM54221.2022.9853080 10.1007/978-3-031-27762-7_3 10.1109/ICRAMI52622.2021.9585913 10.1109/ICAECA56562.2023.10200940 10.1109/ICICICT54557.2022.9918009 10.1109/ICCMC51019.2021.9418447 10.1109/ECAI58194.2023.10194213 10.1109/IMPACT55510.2022.10029087 10.1109/ICOEI56765.2023.10125783 10.1109/ICAECA56562.2023.10200302 |
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SubjectTerms | Artificial Intelligence Breast Cancer CNN Computational Intelligence Control Decision making Engineering Malignant Mammography Mathematical Logic and Foundations Mechatronics Research Article Robotics |
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Title | Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements |
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