Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy
Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours...
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/8/2988 |
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| author | Adarsh Vulli Parvathaneni Naga Srinivasu Madipally Sai Krishna Sashank Jana Shafi Jaeyoung Choi Muhammad Fazal Ijaz |
| author_facet | Adarsh Vulli Parvathaneni Naga Srinivasu Madipally Sai Krishna Sashank Jana Shafi Jaeyoung Choi Muhammad Fazal Ijaz |
| author_sort | Adarsh Vulli |
| collection | DOAJ |
| description | Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably. |
| first_indexed | 2024-03-09T04:13:47Z |
| format | Article |
| id | doaj.art-2064c584d255415f9c9b41afee80a74a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:13:47Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2064c584d255415f9c9b41afee80a74a2023-12-03T13:57:09ZengMDPI AGSensors1424-82202022-04-01228298810.3390/s22082988Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle PolicyAdarsh Vulli0Parvathaneni Naga Srinivasu1Madipally Sai Krishna Sashank2Jana Shafi3Jaeyoung Choi4Muhammad Fazal Ijaz5Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam 530045, IndiaDepartment of Computer Science and Engineering-AIML, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, IndiaDepartment of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam 530045, IndiaDepartment of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul Aziz University, Wadi Ad-Dawasir 11991, Saudi ArabiaSchool of Computing, Gachon University, Seongnam-si 13120, KoreaDepartment of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, KoreaLymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.https://www.mdpi.com/1424-8220/22/8/2988DenseNet-169computational histopathologycancerwhole-slide imageslymph nodesFastAI |
| spellingShingle | Adarsh Vulli Parvathaneni Naga Srinivasu Madipally Sai Krishna Sashank Jana Shafi Jaeyoung Choi Muhammad Fazal Ijaz Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy Sensors DenseNet-169 computational histopathology cancer whole-slide images lymph nodes FastAI |
| title | Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy |
| title_full | Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy |
| title_fullStr | Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy |
| title_full_unstemmed | Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy |
| title_short | Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy |
| title_sort | fine tuned densenet 169 for breast cancer metastasis prediction using fastai and 1 cycle policy |
| topic | DenseNet-169 computational histopathology cancer whole-slide images lymph nodes FastAI |
| url | https://www.mdpi.com/1424-8220/22/8/2988 |
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