An increase in the usage of electronic devices in today’s world leads to a negative impacts on humans. Due to the over-usage of the devices leads to an uncontrollable handling behavior towards them called obsession. This paper deals with the statistical study about the obsession among the group of students and applying image processing techniques for recognition or detection of different types diseases/ symptoms caused by the obsession and explaining the consequences meeting with the obsession of the electronic devices among students normal life.

Functional magnetic resonance imaging has become a very popular tool in neurological and medical analysis over the years.  According to collated data, in the year 1993, as few as 20 papers were presented on the topic of fmri analysis; However, a decade later, as many as 1800 research papers talk about fmri analysis – an exponential increase. An analysis of the activated regions within the brain can be used to detect the its reactions to various stimuli with greater confidence compared to other methods but the success of accurately identifying brain stimuli however lies in the efficiency of the image processing algorithms applied to extract information from the fMRI scans. This paper analyzes the effectiveness of commonly used image processing algorithms in fMRI studies by statistically analyzing their effectiveness in extracting ROI’s in various images (sample size = 17) and tries to project the efficiency of these systems in fMRI scanning.

Ovarian teratomas are said to originate from primordial germ cells and typically contains mature (or less frequently immature) tissue derivatives of three germ cell layers (ectoderm, mesoderm and endoderm). This report includes a series of six mature ovarian teratomas, which were diagnosed in a rural tertiary health care centre over a year and highlights three (out of six) unique cases with intestinal dysplasia, gliomatosis peritonei and struma ovarii respectively. Evaluation and possible mechanism of such rare teratomas are discussed with a brief review of literature.

The study proposes an innovative approach using MATLAB to automate the counting of leukemia cells in blood samples, employing Support Vector Machine (SVM) and Nearest Neighbor algorithms. The method involves preprocessing blood sample images to enhance contrast and apply image filters, followed by segmentation techniques for isolating individual cells. SVM and nearest neighbor algorithms are trained using extracted features such as cell size, shape, and texture. Accurate detection and counting of leukemia cells play a crucial role in leukemia diagnosis and management. Leukemia is a group of cancers characterized by abnormal white blood cell proliferation in the bone marrow, leading to symptoms like bleeding, bruising, fatigue, and increased infection risk due to insufficient normal blood cells. Diagnosis typically involves blood tests or bone marrow biopsy. In clinical bioinformatics, SVM algorithms have enabled the development of robust experimental cancer diagnostic models, utilizing gene expression data with a small number of samples and numerous variables. Efficient implementations of SVM algorithms further facilitate practical application. Support Vector Machines excel in mapping data to higher-dimensional spaces through kernel functions, allowing the identification of maximum-margin hyperplanes for separating training instances.

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