As the world continues to battle devastating effects of COVID-19 pandemic, it has become increasingly crucial screen patients for contamination accurately and effectively. One primary screening methods is chest radiography, utilizing radiological imaging detect presence virus in lungs. This study presents a cutting-edge solution classify infections X-ray images by Gray-Level Co-occurrence Matri...

An autonomous car is a vehicle that can guide itself without human intervention. Various types of steeringless vehicles are being developed. The system the future where computers take over art driving. problem was before it became concern in cars to get high safety. Autonomous need an early warning avoid accidents front car, especially systems be used on highway locations. In this paper, we pro...

Developing a Computer Aided Diagnosis (CAD) system for Breast Ultrasound (BUS) is a high exigent work in medical imaging analysis systems. BUS detects abnormalities or suspicious regions accurately that is misspelled by mammogram, and helps to find the varying degrees of malignancy. Extracting discriminating features in CAD is a most prominent task that results in good diagnosis. This study ana...

We have analyzed first and second order multiresolution texture features in order to discriminate normal and pathological liver cell nuclei. Several combinations of first and second order texture features discriminate animals from the four different groups. The best feature pair (graylevel Variance of the euchromatine and Diagonal moment of the GLCM) gave a correct classification result of 95 %...

This research builds a system for identifying the maturity level of areca fruit based on digital image processing using texture and color features through Gray Level Co-Occurrence Matrix (GLCM) Color moments. The initial stage is pre-processing so that it can be processed to next stage, namely feature extraction. Texture extraction was performed (GLCM), correlation value moments, mean used in t...

This paper presents a methodology to recognize certain crop fields’ images using texture, color and combination of both types of features. In this work, we have considered eight varieties of crop images, namely, Brinjal, Cotton, Groundnut, Paddy, Soyabean, Sugarcane and Sunflower. Texture features using GLCM and color features using HSV are deployed. Artificial Neural Network (ANN) is used for ...

Early detection of breast cancer cells can be predicted through a precise feature extraction technique that produce efficient features. The application Gabor filters, gray level co-occurrence matrices (GLCM) and other textural techniques have proven to achieve promising results but were often characterized by high false-positive rate (FPR) false-negative (FNR) with computational complexities. T...

Day by day the use of image processing is increasing. Now a days image processing is the part and parcel of medical science. By image processing many types of cancer are easily detected. Skin cancer is one of them. In this paper the proposed method detects two types of skin one is cancerous skin and another is affected but not cancerous skin. Skin cancers are most common cancer in human. Skin c...

Lung cancer is the most common disease with greater morality rate. Computed Tomography (CT) images are used for early diagnosis of lung cancer with the help of CAD system. Selection of effective feature set and proper classifier for medical images where machine learning techniques are used is a challenging task. Texture analysis of computed tomography (CT) images is one of the important prelimi...