Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis
Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis...
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| Vydáno v: | International journal of imaging systems and technology Ročník 35; číslo 6 |
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| Abstract | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis. It integrates an improved optimization method with a hybridized CNN architecture. In this article, a custom CNN, feed‐forward and backpropagation have been implemented. The scaled conjugate algorithm is employed in the feed‐forward paradigm, yielding a formidable accuracy of 99.1%. On the other hand, backpropagation implements stochastic gradient descent and exhibits a remarkable accuracy rate of 97.3%. Additionally, by integrating the grey wolf optimization (GWO) algorithm with the Backpropagation Neural Network (BPNN), model performance is enhanced by optimizing parameters and accuracy to 100%. Furthermore, the custom CNN achieves an incredible 98% accuracy by utilizing the Adam optimizer in conjunction with the ReduceLROnPlateau approach. Statistical analysis utilizing Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) tests has demonstrated that the suggested hybrid model improves detection accuracy and reliability. These results highlight the adaptability and effectiveness of various optimization techniques in enhancing the performance of neural network models on a range of demanding tasks related to machine learning and pattern recognition. |
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| AbstractList | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To enable oncologists to diagnose abnormalities promptly, this research proposes an innovative classification framework for breast cancer diagnosis. It integrates an improved optimization method with a hybridized CNN architecture. In this article, a custom CNN, feed‐forward and backpropagation have been implemented. The scaled conjugate algorithm is employed in the feed‐forward paradigm, yielding a formidable accuracy of 99.1%. On the other hand, backpropagation implements stochastic gradient descent and exhibits a remarkable accuracy rate of 97.3%. Additionally, by integrating the grey wolf optimization (GWO) algorithm with the Backpropagation Neural Network (BPNN), model performance is enhanced by optimizing parameters and accuracy to 100%. Furthermore, the custom CNN achieves an incredible 98% accuracy by utilizing the Adam optimizer in conjunction with the ReduceLROnPlateau approach. Statistical analysis utilizing Analysis of Variance (ANOVA) and Honestly Significant Difference (HSD) tests has demonstrated that the suggested hybrid model improves detection accuracy and reliability. These results highlight the adaptability and effectiveness of various optimization techniques in enhancing the performance of neural network models on a range of demanding tasks related to machine learning and pattern recognition. |
| Author | Usman, Muhammad Pu, Juhua Batool, Shumaila Zainab, Saima |
| Author_xml | – sequence: 1 givenname: Shumaila surname: Batool fullname: Batool, Shumaila organization: Department of Mathematics The Women University Multan Multan Pakistan – sequence: 2 givenname: Saima surname: Zainab fullname: Zainab, Saima organization: Department of Mathematics The Women University Multan Multan Pakistan – sequence: 3 givenname: Muhammad orcidid: 0000-0001-7776-6811 surname: Usman fullname: Usman, Muhammad organization: School of Computer Science and Engineering Beihang University Beijing China – sequence: 4 givenname: Juhua surname: Pu fullname: Pu, Juhua organization: School of Computer Science and Engineering Beihang University Beijing China |
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| Cites_doi | 10.1007/s10853‐024‐09799‐8 10.1016/j.iswa.2022.20 10.3390/cancers15030885 10.1016/j.asej.2024.102734 10.32604/cmc.2023.031723 10.3390/diagnostics13132191 10.1109/ACCESS.2020.3021557 10.1109/ACCESS.2023.3310429 10.1016/j.mlwa.2024.100555 10.1007/s11042‐023‐16431‐5 10.1007/s00521‐022‐07895‐x 10.1016/j.advengsoft.2013.12.007 10.3390/jimaging5030037 10.1016/j.compbiomed.2022.105812 10.1038/s41598‐020‐67441‐4 10.1101/2022.04.29.22274513 10.3390/diagnostics13091582 10.1007/s00521‐023‐08771‐y 10.3390/app12041957 10.61356/j.mawa.2024.3237 10.3390/app13010156 10.1109/ACCESS.2022.3212081 10.1109/ITSIM.2010.5561599 10.54021/seesv5n1‐008 10.1038/s41598-024-67424-9 10.17559/TV‐20230621000751 10.1002/ima.23133 10.1109/ACCESS.2023.3298955 10.31557/APJCP.2023.24.2.531 10.1007/s10916‐011‐9768‐0 10.3390/healthcare8020111 10.1007/s00521‐022‐07445‐5 10.1007/s12652‐022‐03713‐3 10.1007/s00530‐024‐01295‐y 10.1186/s12880‐023‐00964‐0 10.3390/diagnostics13172746 10.1016/j.bbe.2023.12.003 10.1016/j.physa.2019.123592 10.1007/s42979‐020‐00305‐w 10.1007/s00521‐023‐09202‐8 10.36227/techrxiv.23283533 |
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| References | e_1_2_16_25_1 e_1_2_16_23_1 Yu M. (e_1_2_16_29_1) 2017 e_1_2_16_27_1 Salaheldin A. M. (e_1_2_16_44_1) 2024; 34 e_1_2_16_42_1 e_1_2_16_21_1 e_1_2_16_40_1 Lu S.‐Y. (e_1_2_16_18_1) 2022; 148 Atteia G. (e_1_2_16_46_1) 2023; 75 e_1_2_16_15_1 e_1_2_16_38_1 Azmi M. S. B. M. (e_1_2_16_35_1) 2010 Mirjalili S. (e_1_2_16_37_1) 2014; 69 e_1_2_16_13_1 e_1_2_16_19_1 Shah A. (e_1_2_16_50_1) 2025 e_1_2_16_17_1 e_1_2_16_36_1 e_1_2_16_30_1 e_1_2_16_11_1 Singhal S. (e_1_2_16_32_1) 2018 e_1_2_16_7_1 e_1_2_16_9_1 e_1_2_16_3_1 e_1_2_16_5_1 e_1_2_16_26_1 e_1_2_16_24_1 Saleh N. (e_1_2_16_43_1) 2024; 14 Khodadadi N. (e_1_2_16_51_1) 2023; 11 e_1_2_16_45_1 e_1_2_16_28_1 e_1_2_16_41_1 e_1_2_16_22_1 e_1_2_16_20_1 Khodadadi N. (e_1_2_16_52_1) 2022; 10 e_1_2_16_14_1 e_1_2_16_39_1 El‐Kenawy E. S. M. (e_1_2_16_48_1) 2025; 9 e_1_2_16_12_1 e_1_2_16_16_1 e_1_2_16_31_1 e_1_2_16_33_1 e_1_2_16_10_1 Awujoola O. J. (e_1_2_16_2_1) 2024 Abdalla O. A. (e_1_2_16_34_1) 2010 Abdelhamid A. A. (e_1_2_16_47_1) 2023; 11 e_1_2_16_8_1 e_1_2_16_4_1 e_1_2_16_6_1 Dutta P. K. (e_1_2_16_49_1) 2025; 4 |
| References_xml | – ident: e_1_2_16_39_1 doi: 10.1007/s10853‐024‐09799‐8 – ident: e_1_2_16_12_1 doi: 10.1016/j.iswa.2022.20 – ident: e_1_2_16_13_1 doi: 10.3390/cancers15030885 – ident: e_1_2_16_25_1 doi: 10.1016/j.asej.2024.102734 – volume: 75 start-page: 1883 issue: 1 year: 2023 ident: e_1_2_16_46_1 article-title: Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data publication-title: Computers, Materials & Continua doi: 10.32604/cmc.2023.031723 – ident: e_1_2_16_7_1 doi: 10.3390/diagnostics13132191 – ident: e_1_2_16_9_1 doi: 10.1109/ACCESS.2020.3021557 – volume: 11 start-page: 94094 year: 2023 ident: e_1_2_16_51_1 article-title: BAOA: Binary Arithmetic Optimization Algorithm With K‐Nearest Neighbor Classifier for Feature Selection publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3310429 – ident: e_1_2_16_26_1 doi: 10.1016/j.mlwa.2024.100555 – ident: e_1_2_16_24_1 doi: 10.1007/s11042‐023‐16431‐5 – ident: e_1_2_16_30_1 – volume: 4 start-page: 33 year: 2025 ident: e_1_2_16_49_1 article-title: Ethical Challenges and Regulatory Compliance in AI‐Driven Neurological Diagnostics: A Review of Standards and Practices publication-title: Metaheuristic Optimization Review – start-page: 221 volume-title: Machine Learning Algorithms Using Scikit and TensorFlow Environments year: 2024 ident: e_1_2_16_2_1 – ident: e_1_2_16_16_1 doi: 10.1007/s00521‐022‐07895‐x – volume: 69 start-page: 46 year: 2014 ident: e_1_2_16_37_1 article-title: Grey Wolf Optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – ident: e_1_2_16_23_1 doi: 10.3390/jimaging5030037 – volume: 148 year: 2022 ident: e_1_2_16_18_1 article-title: SAFNet: A Deep Spatial Attention Network With Classifier Fusion for Breast Cancer Detection publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105812 – ident: e_1_2_16_20_1 doi: 10.1038/s41598‐020‐67441‐4 – ident: e_1_2_16_31_1 doi: 10.1101/2022.04.29.22274513 – ident: e_1_2_16_19_1 doi: 10.3390/diagnostics13091582 – ident: e_1_2_16_27_1 – volume-title: Breast Cancer Prediction Using Machine Learning Algorithm. Approved by Supervising Committee: M. J. Daniels and E. C. Theriot year: 2017 ident: e_1_2_16_29_1 – ident: e_1_2_16_17_1 doi: 10.1007/s00521‐023‐08771‐y – start-page: 164 volume-title: 2010 IEEE Student Conference on Research and Development (SCOReD) year: 2010 ident: e_1_2_16_35_1 – ident: e_1_2_16_5_1 doi: 10.3390/app12041957 – ident: e_1_2_16_14_1 doi: 10.61356/j.mawa.2024.3237 – ident: e_1_2_16_6_1 doi: 10.3390/app13010156 – volume: 10 start-page: 106673 year: 2022 ident: e_1_2_16_52_1 article-title: An Archive‐Based Multi‐Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3212081 – start-page: 994 volume-title: 2010 International Symposium on Information Technology year: 2010 ident: e_1_2_16_34_1 doi: 10.1109/ITSIM.2010.5561599 – ident: e_1_2_16_40_1 doi: 10.54021/seesv5n1‐008 – volume: 14 issue: 1 year: 2024 ident: e_1_2_16_43_1 article-title: Skin Cancer Classification Based on an Optimized Convolutional Neural Network and Multicriteria Decision‐Making publication-title: Scientific Reports doi: 10.1038/s41598-024-67424-9 – ident: e_1_2_16_36_1 doi: 10.17559/TV‐20230621000751 – volume: 34 issue: 4 year: 2024 ident: e_1_2_16_44_1 article-title: Deep Learning‐Based Automated Detection and Grading of Papilledema From OCT Images: A Promising Approach for Improved Clinical Diagnosis and Management publication-title: International Journal of Imaging Systems and Technology doi: 10.1002/ima.23133 – volume: 11 start-page: 79750 year: 2023 ident: e_1_2_16_47_1 article-title: Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3298955 – ident: e_1_2_16_4_1 doi: 10.31557/APJCP.2023.24.2.531 – volume: 9 start-page: 1 year: 2025 ident: e_1_2_16_48_1 article-title: Hybrid Ensemble Learning for Flow‐Level IoT Traffic Classification Using ACI Dataset: Towards Scalable and Real‐Time Threat Detection publication-title: Journal of Artificial Intelligence and Metaheuristics – ident: e_1_2_16_33_1 doi: 10.1007/s10916‐011‐9768‐0 – ident: e_1_2_16_45_1 doi: 10.3390/healthcare8020111 – ident: e_1_2_16_15_1 doi: 10.1007/s00521‐022‐07445‐5 – ident: e_1_2_16_22_1 doi: 10.1007/s12652‐022‐03713‐3 – ident: e_1_2_16_8_1 doi: 10.1007/s00530‐024‐01295‐y – ident: e_1_2_16_21_1 doi: 10.1186/s12880‐023‐00964‐0 – start-page: 464 volume-title: 2018 2nd International Conference on I‐SMAC (IoT in Social, Mobile, Analytics and Cloud) year: 2018 ident: e_1_2_16_32_1 – ident: e_1_2_16_28_1 – ident: e_1_2_16_3_1 doi: 10.3390/diagnostics13172746 – ident: e_1_2_16_11_1 doi: 10.1016/j.bbe.2023.12.003 – ident: e_1_2_16_42_1 doi: 10.1016/j.physa.2019.123592 – volume-title: Breast Ultrasound Images Dataset year: 2025 ident: e_1_2_16_50_1 – ident: e_1_2_16_10_1 doi: 10.1007/s42979‐020‐00305‐w – ident: e_1_2_16_38_1 doi: 10.1007/s00521‐023‐09202‐8 – ident: e_1_2_16_41_1 doi: 10.36227/techrxiv.23283533 |
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| Snippet | Convolutional neural networks (CNNs) have augmented conventional approaches in medical imaging by improving tumor detection and classification efficacy. To... |
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| SubjectTerms | Abnormalities Accuracy Algorithms Artificial neural networks Back propagation Back propagation networks Breast cancer Classification Diagnosis Effectiveness Machine learning Medical diagnosis Medical imaging Neural networks Optimization Optimization techniques Pattern recognition Performance enhancement Statistical analysis Tumors Variance analysis |
| Title | Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis |
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