The different types of white blood cells equips us an important data for diagnosing and identifying of many diseases. The automation of this task can save time and avoid errors in the identification process. In this paper, we explore whether using shape features of nucleus is sufficient to classify white blood cells or not. According to this, an automatic system is implemented that is able to identify and analyze White Blood Cells (WBCs) into five categories (Basophil, Eosinophil, Lymphocyte, Monocyte, and Neutrophil). Four steps are required for such a system; the first step represents the segmentation of the cell images and the second step involves the scanning of each segmented image to prepare its dataset. Extracting the shapes and textures from scanned image are performed in the third step. Finally, different machine learning algorithms such as (K* classifier, Additive Regression, Bagging, Input Mapped Classifier, or Decision Table) is separately applied to the extracted (shapes and textures) to obtain the results. Each algorithm results are compared to select the best one according to different criteria’s.

Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.

Novel Coronavirus disease 2019 (COVID-19) is a type of pandemic viruses that cause respiratory tract infection in humans. The clinical imaging of Chest X-Ray (CXR) by Computer Aided Diagnosis (CAD) plays an important role to identify the patients who infected by COVID-19. The objective of this paper presents a Computer Aided Diagnosis (CAD) method for automatically classify 110 frontal CXR images of contagious people according to Normal and COVID-19 infection. The proposed method contains of four phases: image enhancement, feature extraction, feature selection and classification. Gaussian filter is performed to de-noise the images and Adaptive Histogram Equalization (AHE) for image enhancement in pre-processing step for better decision-making process. Local Binary Pattern (LBP) features set are extracted from the dataset. Binary Particle Swarm Optimization (BPSO) is considered to select the clinically relevant features and developing the robust model. The successive features are fed to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The experimental results show that the system robustness in classification COVID-19 from Normal images with average accuracy 94.6%, sensitivity 96.2% and specificity 93%.