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Title: :  FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis
PaperId: :  22753
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22753
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
MUKKARA GAYATHRIVasireddy Venkatadri Institute Of Technology
MELAM STERINA LILLYVasireddy Venkatadri Institute Of Technology
NALABOLU SRI LEKHAVasireddy Venkatadri Institute Of Technology
GOGINENI SIVA BHAVANIVasireddy Venkatadri Institute Of Technology

Abstract

Cmputer Vision
Mammography, CNN, Deep Learning, EfficientNetB0, MobileNetV2, inceptionV3, Transfer Learning, ensemble model
Breast cancer remains a significant global health challenge, necessitating accurate and efficient detection methods to improve patient outcomes. Mammography serves as a cornerstone for early diagnosis, yet the interpretation of mammograms can be prone to errors, leading to both false positives and missed diagnoses. In response to this critical issue, this study focuses on harnessing the power of Convolutional Neural Networks (CNNs) for the automated detection of breast cancer in mammographic images. The research investigates a diverse range of deep learning techniques, including popular network architectures such as VGG19, ResNet152, InceptionV3, DenseNet121, MobileNetV2, and EfficientNetB0. Various factors crucial to model performance are explored, such as class weighting strategies, input image dimensions, preprocessing methodologies, transfer learning approaches, dropout rates, and the impact of different mammogram projections. Through a systematic and comprehensive analysis, this project aims to evaluate the effectiveness and efficiency of these deep learning methodologies in the context of breast cancer detection. By employing a divide-and-conquer approach, the study seeks to gain valuable insights into selecting the most suitable techniques for enhancing detection accuracy while minimizing the need for extensive trial and error experimentation. The ultimate goal of this research is to advance automated breast cancer screening by optimizing deep learning models for mammogram analysis. By understanding the nuances of various parameters and their impact on model performance, this study aims to contribute to improved diagnostic accuracy and ultimately enhance patient care in the realm of breast cancer detection. The proposed FusedMammoNet model achieved a test accuracy of 96%, recorded highest AUC-ROC ranged from 0.98-1.00 and both precision and recall ranging from 93% to 94%.

Citations

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IJARIIE MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. "FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 431-436
MLA MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. "FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 431-436.
APA MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, & GOGINENI SIVA BHAVANI. (2024). FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 431-436.
Chicago MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. "FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 431-436.
Oxford MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. 'FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 431-436. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/FusedMammoNet__Ensemble_of_diverse_models_for_multi_class_mammogram_analysis_ijariie22753.pdf (Accessed : ).
Harvard MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. (2024) 'FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 431-436IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/FusedMammoNet__Ensemble_of_diverse_models_for_multi_class_mammogram_analysis_ijariie22753.pdf (Accessed : )
IEEE MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI, "FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 431-436, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/FusedMammoNet__Ensemble_of_diverse_models_for_multi_class_mammogram_analysis_ijariie22753.pdf [Accessed : ].
Turabian MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. "FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver MUKKARA GAYATHRI, MELAM STERINA LILLY, NALABOLU SRI LEKHA, and GOGINENI SIVA BHAVANI. FusedMammoNet: Ensemble of diverse models for multi-class mammogram analysis. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 431-436. Available from: https://ijariie.com/AdminUploadPdf/FusedMammoNet__Ensemble_of_diverse_models_for_multi_class_mammogram_analysis_ijariie22753.pdf
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