Detection of Malaria Diseases with Residual Attention Network

Abstract

To describe a model using classic machine learning techniques for creating machine learning systems, a person who specializes in this technique needs to extract feature vectors. This period also breaks into expert time. Also, these methods could not process raw data without preprocessing and expert assistance. Deep learning has made great progress in solving problems at this point, and machine learning research has continued for many years. Unlike traditional machine learning and image processing techniques, deep networks enable learning processes using raw data. In this study, a deep learning approach for the classification and diagnosis of malaria is developed. For this purpose, Residual Attention Network (RAN) a deep learning Convolutional Neural Network (CNN) technique was used with previously 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, the RAN 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) obtained only 83.30% classification accuracy rate. Besides, it is evaluated that for the classification of blood cell images and diagnosis of malaria using deep learning methods can be used successfully. In addition, deep learning methods have the advantage of automatically learning features from input data and require minimal input by specialists in automated malaria diagnosis.

CNN-based bi-directional and directional long-short term memory network for determination of face mask

“…CNN, which is a deep learning method, is highly preferred in different discipline applications at present since it can easily distinguish small details that the human eye cannot notice in image recognition applications. The fact that they do not require much preprocessing and recognize visual patterns directly from pixel images is the most important feature of CNNs [32] , [33] , [34] , [35] .…”

Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

“…MobileNetV2 showed higher performance with fewer parameters compared to the previously developed version of MobileNetV1. MobileNetV2 is a CNN architecture developed specifically to achieve good performance on mobile devices (Qanbar and Tasdemir, 2019). In deep neural networks, it is seen that neurons that are not highly activated during training become ineffective, and in this case, residual value occurs in the network.…”

Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning

“…Kehadiran sistem diagnosis berbasis pengolahan citra ini diharapkan mampu mempercepat proses screening awal apakah seseorang sudah terinfeksi parasit plasmodium. Penelitian yang dilakukan oleh Tasdemir Qanbar dan melakukan pendekatan deep learning dengan menggunakan lima arsitektur berbeda, yaitu; Alexnet, VGG16, MobileNetV2, ResNet50 dan RAN (Qanbar, 2019). Hasil pengujian diperoleh bahwa RAN menghasilkan akurasi terbaik sebesar 95,51% dan MobileNetV2 memberikan performansi terburuk dengan akurasi sebesar 50%.…”