A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis
The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accura...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/9299621 |
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| _version_ | 1849683433903620096 |
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| author | Almas Begum V. Dhilip Kumar Junaid Asghar D. Hemalatha G. Arulkumaran |
| author_facet | Almas Begum V. Dhilip Kumar Junaid Asghar D. Hemalatha G. Arulkumaran |
| author_sort | Almas Begum |
| collection | DOAJ |
| description | The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. |
| format | Article |
| id | doaj-art-d82da28e9b7641b08b47d820ff1b850c |
| institution | DOAJ |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d82da28e9b7641b08b47d820ff1b850c2025-08-20T03:23:52ZengWileyComplexity1099-05262022-01-01202210.1155/2022/9299621A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer DiagnosisAlmas Begum0V. Dhilip Kumar1Junaid Asghar2D. Hemalatha3G. Arulkumaran4Computer Science and EngineeringComputer Science and EngineeringFaculty of PharmacyComputer Science and EngineeringDepartment of Electrical and Computer EngineeringThe most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily.http://dx.doi.org/10.1155/2022/9299621 |
| spellingShingle | Almas Begum V. Dhilip Kumar Junaid Asghar D. Hemalatha G. Arulkumaran A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis Complexity |
| title | A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis |
| title_full | A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis |
| title_fullStr | A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis |
| title_full_unstemmed | A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis |
| title_short | A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis |
| title_sort | combined deep cnn lstm with a random forest approach for breast cancer diagnosis |
| url | http://dx.doi.org/10.1155/2022/9299621 |
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