MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks
Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networ...
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| Language: | English |
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7022 |
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| author | Rachele Catalano Myriam Giusy Tibaldi Lucia Lombardi Antonella Santone Mario Cesarelli Francesco Mercaldo |
| author_facet | Rachele Catalano Myriam Giusy Tibaldi Lucia Lombardi Antonella Santone Mario Cesarelli Francesco Mercaldo |
| author_sort | Rachele Catalano |
| collection | DOAJ |
| description | Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer. |
| format | Article |
| id | doaj-art-3bd34ba723eb44559e094c1e27e457ac |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3bd34ba723eb44559e094c1e27e457ac2024-11-08T14:41:53ZengMDPI AGSensors1424-82202024-10-012421702210.3390/s24217022MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural NetworksRachele Catalano0Myriam Giusy Tibaldi1Lucia Lombardi2Antonella Santone3Mario Cesarelli4Francesco Mercaldo5Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Engineering, University of Sannio, 82100 Benevento, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyBreast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer.https://www.mdpi.com/1424-8220/24/21/7022convolutional neural networksartificial intelligencedeep learningdigital pathologybreast cancer |
| spellingShingle | Rachele Catalano Myriam Giusy Tibaldi Lucia Lombardi Antonella Santone Mario Cesarelli Francesco Mercaldo MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks Sensors convolutional neural networks artificial intelligence deep learning digital pathology breast cancer |
| title | MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks |
| title_full | MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks |
| title_fullStr | MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks |
| title_full_unstemmed | MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks |
| title_short | MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks |
| title_sort | mr net a method for breast cancer detection and localization from histological images through explainable convolutional neural networks |
| topic | convolutional neural networks artificial intelligence deep learning digital pathology breast cancer |
| url | https://www.mdpi.com/1424-8220/24/21/7022 |
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