Detection of Malaria Diseases with Residual Attention Network
Yazarlar (2)
Mohanad Mohammed Qanbar
Diğer
Diğer
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Diğer hakemli uluslarası dergilerde yayınlanan tam makale)
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| Dergi Adı | International Journal ofIntelligent Systems and Applications in Engineering | ||
| Dergi ISSN | 2147-6799 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2019 |
| Cilt / Sayı / Sayfa | 7 / 2 / 238–244 | DOI | – |
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| To describe a model using classic machine learning techniques for creating machine learning systems, a person who specializesin this technique needs to extract feature vectors. This period also breaks into expert time. Also, these methods could not process raw datawithout preprocessing and expert assistance. Deep learning has made great progress in solving problems at this point, and machine learningresearch has continued for many years. Unlike traditional machine learning and image processing techniques, deep networks enablelearning processes using raw data. In this study, a deep learning approach for the classification and diagnosis of malaria is developed. Forthis purpose, Residual Attention Network (RAN) a deep learning Convolutional Neural Network (CNN) technique was used withpreviously classified datasets. The goal is to design computer-aided software for classifying blood cell images (blood samples) as“parasitized” or “uninfected”. In the program, a decision support system was implemented by a deep learning approach. As a result, theRAN model achieved the best ability to produce better results in processing and classification images compared to other algorithm types.RAN model’s training simulation results showed a 95.79% classification accuracy rate. Using the Support Vector Machine (SVM) obtainedonly 83.30% classification accuracy rate. Besides, it is evaluated that for the classification of blood cell images and diagnosis of malariausing deep learning methods can be used successfully. In addition, deep learning methods have the advantage of automatically learningfeatures from input data and require minimal input by specialists in automated malaria diagnosis. |
| Anahtar Kelimeler |
