Online versus offline Arabic script classification

Rehman

et al. 2019

Self Cite

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K-nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature. K E Y W O R D S KNN and random forest, LBP-HSV features, Plasmodium parasite, region growing and dynamic convolution

Section: Classificationmentioning

confidence: 99%

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Abbas

Rehman

et al. 2019

Self Cite

“…To remove unwanted information present in acquired images, different preprocessing methods are used (Saba, Almazyad, & Rehman, ; Saba, Rehman, Al‐Dhelaan, & Al‐Rodhaan, ; Saba, Rehman, Altameem, & Uddin, ; Saba, Rehman, & Sulong, ). Tahir et al () conducted a comprehensive study of preprocessing techniques and their effect on feature extraction from medical images.…”

Section: Proposed Methodologymentioning

confidence: 99%

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Iqbal

Khan

et al. 2019

Self Cite

Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

“…Multi‐Layer Feed Forward Perceptron (MLP): A Multilayer feed‐forward network is an important class of ANN, which represent nonlinear functional mapping between set of input and output variables. A MLP is generally used in pattern identification and recognition (Saba, Al‐Zahrani, & Rehman, ; Saba, Almazyad, & Rehman, ). An input, hidden and output layers in MLP for digging out useful information during learning process.…”

Section: Proposed Frameworkmentioning

confidence: 99%

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection

Ullah

Islam

et al. 2019

Self Cite

Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity. Similarly, three well‐known classifiers that is, Naïve Bayes classifier, Support Vector Machine, and Artificial Neural Network are used for the classification of EXs. Moreover, an ensemble‐based classifier is used for the selection of best classifier on the basis of majority voting technique. Experiments are performed on three well‐known benchmark datasets and a real dataset developed at local Hospital. It has been observed that the proposed technique achieved an accuracy of 98% in the detection and classification of EXs in color fundus images.