Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images
Recent advances in deep learning, coupled with the onslaught of unlabelled medical data have drawn ever-increasing research interests by discovering multiple levels of distributed representations and solving complex medical related problems. Malaria disease detection in early stage requires an accur...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9097238/ |
| _version_ | 1819158745999998976 |
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| author | Priyadarshini Adyasha Pattanaik Mohit Mittal Mohammad Zubair Khan |
| author_facet | Priyadarshini Adyasha Pattanaik Mohit Mittal Mohammad Zubair Khan |
| author_sort | Priyadarshini Adyasha Pattanaik |
| collection | DOAJ |
| description | Recent advances in deep learning, coupled with the onslaught of unlabelled medical data have drawn ever-increasing research interests by discovering multiple levels of distributed representations and solving complex medical related problems. Malaria disease detection in early stage requires an accurate and precise diagnosis in order to achieve successful patient remission. This paper proposes a comprehensive computer-aided diagnosis (CAD) scheme for identifying the presence of malaria parasites in thick blood smear images. The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder. The optimum size of the CAD scheme used in this research is 12500-2500-100-50-2, where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes. Moreover, the 10- fold cross validation reflects that the classification is reliable and is applicable to new patient blood smear images. The proposed CAD scheme has been evaluated using malaria blood smear image data set, achieving a detection accuracy of 89.10%, a sensitivity of 93.90% and specificity of 83.10%. The extensive comparative experiment suggests that the proposed CAD scheme provides richer effectiveness and efficiency for malaria data set compared to other deep learning techniques for better diagnosis decision and management. This work implements a novel approach to fast processing and will be a beneficial tool in disease identification. |
| first_indexed | 2024-12-22T16:29:33Z |
| format | Article |
| id | doaj.art-4bec5b8b65214457bf50567e9183252e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:29:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4bec5b8b65214457bf50567e9183252e2022-12-21T18:20:06ZengIEEEIEEE Access2169-35362020-01-018949369494610.1109/ACCESS.2020.29960229097238Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic ImagesPriyadarshini Adyasha Pattanaik0Mohit Mittal1https://orcid.org/0000-0003-0878-4615Mohammad Zubair Khan2https://orcid.org/0000-0002-2409-7172Telecom SudParis, Évry, FranceDepartment of Information Science and Engineering, Kyoto Sangyo University, Kyoto, JapanDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaRecent advances in deep learning, coupled with the onslaught of unlabelled medical data have drawn ever-increasing research interests by discovering multiple levels of distributed representations and solving complex medical related problems. Malaria disease detection in early stage requires an accurate and precise diagnosis in order to achieve successful patient remission. This paper proposes a comprehensive computer-aided diagnosis (CAD) scheme for identifying the presence of malaria parasites in thick blood smear images. The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder. The optimum size of the CAD scheme used in this research is 12500-2500-100-50-2, where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes. Moreover, the 10- fold cross validation reflects that the classification is reliable and is applicable to new patient blood smear images. The proposed CAD scheme has been evaluated using malaria blood smear image data set, achieving a detection accuracy of 89.10%, a sensitivity of 93.90% and specificity of 83.10%. The extensive comparative experiment suggests that the proposed CAD scheme provides richer effectiveness and efficiency for malaria data set compared to other deep learning techniques for better diagnosis decision and management. This work implements a novel approach to fast processing and will be a beneficial tool in disease identification.https://ieeexplore.ieee.org/document/9097238/Computer-aided diagnosis (CAD)Deep learningmalaria parasite detectionmicroscopic blood smear imagesdigital pathologyK-fold cross-validation |
| spellingShingle | Priyadarshini Adyasha Pattanaik Mohit Mittal Mohammad Zubair Khan Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images IEEE Access Computer-aided diagnosis (CAD) Deep learning malaria parasite detection microscopic blood smear images digital pathology K-fold cross-validation |
| title | Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images |
| title_full | Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images |
| title_fullStr | Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images |
| title_full_unstemmed | Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images |
| title_short | Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images |
| title_sort | unsupervised deep learning cad scheme for the detection of malaria in blood smear microscopic images |
| topic | Computer-aided diagnosis (CAD) Deep learning malaria parasite detection microscopic blood smear images digital pathology K-fold cross-validation |
| url | https://ieeexplore.ieee.org/document/9097238/ |
| work_keys_str_mv | AT priyadarshiniadyashapattanaik unsuperviseddeeplearningcadschemeforthedetectionofmalariainbloodsmearmicroscopicimages AT mohitmittal unsuperviseddeeplearningcadschemeforthedetectionofmalariainbloodsmearmicroscopicimages AT mohammadzubairkhan unsuperviseddeeplearningcadschemeforthedetectionofmalariainbloodsmearmicroscopicimages |