Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.

Autism is a lifetime developmental disability which is a great concern in the present world. As of 2010, autism affects 1-2 in every 1000 people all over the world. Autism is highly inherited that appears during infancy or childhood. It is a spectrum condition; some people have learning disabilities, mental health issues and other conditions like hearing impairment, Down's syndrome, epilepsy etc. Recent studies show autistic people encompass faulty connections between different brain regions; corpus callosum region is one of them. To explore the causes of autism, the study of corpus callosum of brain is carried out among the control and autistic individuals in which fMRI (functional Magnetic Resonance Imaging) scan images of corpus callosum is taken as input. Firstly, Sobel, Prewitt and Robert edge detection is performed in the images respectively to detect the surrounding edges of corpus callosum and then thresholding method is applied for segmentation of images. The work is carried out for the children, teenager and adult. Furthermore, the binarization method is used to calculate the area of corpus callosum of brain for the control and autistic patients. Finally, the differences of the produced images of corpus callosum between the control and autistic individuals will help in understanding autism.

Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.

Autism is a great concern to the present world. It is a developmental disorder of human brain that occurs in the first 3 years of life. Autism is an assortment of behavioral problems such as restricted happiness, sensory sensitivities and repetitive behaviors. The Amygdala Region (AMG) along with other cortical regions of human brain plays a vital role in socio-emotional behavior in controls and hypoactive in patients with autism. Functional magnetic resonance imaging (fMRI) shows the young children with autism have a larger amygdala than typically developing children. In pursuing the work, fMRI images of AMG for control and autistic patients are considered as input images for behavior analysis. Four categories of fixation such as, Neutral-fixation, Happy-fixation, Sad-fixation, and Fearful-fixation are considered. Initially, the fMRI images are processed for segmentation using Level Set Method, the produced images of which are further processed for thresholding and morphological operation. The proposed approach has been implemented in MATLAB 9.00. Finally, it has been observed that AMG region of brain is to be hyper functional in autistic patients rather than hypo functional in controlled patients.

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