Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams
Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis...
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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: | Bioinorganic Chemistry and Applications |
| Online Access: | http://dx.doi.org/10.1155/2022/2682287 |
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| author | Ammar Akram Abdulrazzaq Asaad T. Al-Douri Abdulsattar Abdullah Hamad Mustafa Musa Jaber Zelalem Meraf |
| author_facet | Ammar Akram Abdulrazzaq Asaad T. Al-Douri Abdulsattar Abdullah Hamad Mustafa Musa Jaber Zelalem Meraf |
| author_sort | Ammar Akram Abdulrazzaq |
| collection | DOAJ |
| description | Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN. |
| format | Article |
| id | doaj-art-471f6018cd6f4322aad8eb4afac62941 |
| institution | Kabale University |
| issn | 1687-479X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioinorganic Chemistry and Applications |
| spelling | doaj-art-471f6018cd6f4322aad8eb4afac629412025-02-03T01:06:42ZengWileyBioinorganic Chemistry and Applications1687-479X2022-01-01202210.1155/2022/2682287Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological ExamsAmmar Akram Abdulrazzaq0Asaad T. Al-Douri1Abdulsattar Abdullah Hamad2Mustafa Musa Jaber3Zelalem Meraf4Department of Medical Laboratory TechnologiesDepartment of Dental IndustryDepartment of Medical Laboratory TechniquesDepartment of Medical Instruments Engineering TechniquesDepartment of StatisticsSchistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.http://dx.doi.org/10.1155/2022/2682287 |
| spellingShingle | Ammar Akram Abdulrazzaq Asaad T. Al-Douri Abdulsattar Abdullah Hamad Mustafa Musa Jaber Zelalem Meraf Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams Bioinorganic Chemistry and Applications |
| title | Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams |
| title_full | Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams |
| title_fullStr | Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams |
| title_full_unstemmed | Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams |
| title_short | Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams |
| title_sort | assessing deep learning techniques for the recognition of tropical disease in images from parasitological exams |
| url | http://dx.doi.org/10.1155/2022/2682287 |
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