Malaria Parasite Detection Using a Quantum-Convolutional Network

Abstract

Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the RGB localized images were converted into YCbCr channels to normalize the image intensity values. Subsequently, the actual lesion region was segmented using a histogram-based color thresholding approach. The segmented images were employed for classification in two different ways. In the first method, a CNN model is developed by the selection of optimum layers after extensive experimentation, and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of the microscopic malaria images. Second, a quantum-convolutional model is employed for informative feature extraction from microscopic malaria images, and the extracted feature vectors are supplied to the softmax layer for classification. Finally, classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN. The proposed models attain a precision rate greater than 90%, thereby proving that these models performed better than the existing models.

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

“…In Table 3 , deep siamese CNN provided 66.7% accuracy. This method needs improvement to increase the detection accuracy [ 14 ]. Chen et al developed a model to automatically measure KOA severity from knee radiographs and provided an accuracy of 69.7% [ 10 ].…”

“…There has been plenty of work carried out in the area of KOA imaging to identify and classify knee diseases. In image processing, feature extraction is an effective step for image representation [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. For the recognition of diseases, feature extraction is very helpful to machine learning (ML) algorithms.…”

Fight detection with spatial and channel wise attention‐based ConvLSTM model

“…Irfanullah et al (2022) utilized MobileNet model for intelligent reconnaissance video examination in real time to cater a practical solution for the difficult human undertaking. A recent work (Amin et al, 2023), attempted to define Quantum CNNs to address the sufficiently large data or model dimension limitation. It classified the number of violent robberies such as armed thefts containing handguns or knives, and robberies displaying varying levels of viciousness.…”

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

“…In the literature, several optimizations, clustering, and classification techniques are widely used for the analysis of malaria cells, some of which are discussed in this section (8,(10)(11)(12)(13)(14). The classification techniques used for the diagnosis of the malaria cells, in which AdaBoost (15), Naïve Bayes Tree (16), SVM (17), DT (18), and Linear Discriminant (19), classifiers are involved.…”