Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset
Abstract Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer...
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| Format: | Article |
| Language: | English |
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BMC
2022-02-01
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| Series: | BMC Research Notes |
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| Online Access: | https://doi.org/10.1186/s13104-022-05936-1 |
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| author | H. El Agouri M. Azizi H. El Attar M. El Khannoussi A. Ibrahimi R. Kabbaj H. Kadiri S. BekarSabein S. EchCharif C. Mounjid B. El Khannoussi |
| author_facet | H. El Agouri M. Azizi H. El Attar M. El Khannoussi A. Ibrahimi R. Kabbaj H. Kadiri S. BekarSabein S. EchCharif C. Mounjid B. El Khannoussi |
| author_sort | H. El Agouri |
| collection | DOAJ |
| description | Abstract Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of attention. Therefore, our study aimed to design a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. We collected overall 328 digital slides, from 116 of surgical breast specimens diagnosed with invasive breast carcinoma of non-specific type, and referred to the histopathology department of the National Institute of Oncology in Rabat, Morocco. We used two models of deep neural network architectures in order to accurately classify the images into one of three categories: normal tissue-benign lesions, in situ carcinoma or invasive carcinoma. Results Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. We reported high degrees of overall correct classification accuracy (88%), and sensitivity (95%) for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision. The results of the present study showed that the designed classification model has a good generalization performance in predicting diagnosis of breast cancer, in spite of the limited size of the data. To our knowledge, this approach can be highly compared with other common methods in the automated analysis of breast cancer images reported in literature. |
| first_indexed | 2024-12-13T13:12:40Z |
| format | Article |
| id | doaj.art-490140a0d2eb47128886297a448319b6 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-12-13T13:12:40Z |
| publishDate | 2022-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-490140a0d2eb47128886297a448319b62022-12-21T23:44:39ZengBMCBMC Research Notes1756-05002022-02-011511710.1186/s13104-022-05936-1Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private datasetH. El Agouri0M. Azizi1H. El Attar2M. El Khannoussi3A. Ibrahimi4R. Kabbaj5H. Kadiri6S. BekarSabein7S. EchCharif8C. Mounjid9B. El Khannoussi10Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityDatapathologyAnatomic Pathology Laboratory EnnassrDatapathologyMedical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in RabatPathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityPathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityPathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityPathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityPathology Department, Oncology National Institute, Faculty of Sciences, Mohammed V UniversityPathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V UniversityAbstract Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of attention. Therefore, our study aimed to design a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. We collected overall 328 digital slides, from 116 of surgical breast specimens diagnosed with invasive breast carcinoma of non-specific type, and referred to the histopathology department of the National Institute of Oncology in Rabat, Morocco. We used two models of deep neural network architectures in order to accurately classify the images into one of three categories: normal tissue-benign lesions, in situ carcinoma or invasive carcinoma. Results Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. We reported high degrees of overall correct classification accuracy (88%), and sensitivity (95%) for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision. The results of the present study showed that the designed classification model has a good generalization performance in predicting diagnosis of breast cancer, in spite of the limited size of the data. To our knowledge, this approach can be highly compared with other common methods in the automated analysis of breast cancer images reported in literature.https://doi.org/10.1186/s13104-022-05936-1Breast cancerDigital pathologyArtificial intelligenceDeep learningMachine learningConvolutional Neural Networks |
| spellingShingle | H. El Agouri M. Azizi H. El Attar M. El Khannoussi A. Ibrahimi R. Kabbaj H. Kadiri S. BekarSabein S. EchCharif C. Mounjid B. El Khannoussi Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset BMC Research Notes Breast cancer Digital pathology Artificial intelligence Deep learning Machine learning Convolutional Neural Networks |
| title | Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset |
| title_full | Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset |
| title_fullStr | Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset |
| title_full_unstemmed | Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset |
| title_short | Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset |
| title_sort | assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer first moroccan prospective study on a private dataset |
| topic | Breast cancer Digital pathology Artificial intelligence Deep learning Machine learning Convolutional Neural Networks |
| url | https://doi.org/10.1186/s13104-022-05936-1 |
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