https://repositorio-aberto.up.pt/bitstream/10216/124569/3/369051.1.pdf

Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images

Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding; (iii) Image segmentation to extract infected region(s); and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden.

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images.

The human brain is considered to be the anatomical seat of intelligence, comprehensively supervising conscious and autonomous functions responsible for monitoring and control operations. Although neural homeostasis can be disrupted, early signs of disease should be recognized to save the patient from permanent disability and even a preventable death. The record of World Health Organization (WHO) lists various brain diseases, such as aneurism, stroke and tumor, which affect humans irrespective of their age, sex and province, all of which affect diagnosis, prognosis and treatment options. Since clinically significant diagnosis of brain abnormality is generally performed using dedicated imaging procedures and also under the supervision of an experienced radiologist, more accurate tools can make this process even more precise. The usual protocol involves a radiologist who records the three-dimensional (3D) image which provides initial insight on the type of brain disease, followed by doctor examination of the 3D/2D image that determines the treatment plan. This article proposes a tool and associated procedure to examine a clinical brain image with improved accuracy in order to provide early insight on ideal treatment procedure. In summary, this tool gives the treatment team unprecedented assessment capability before an operation by integrating all the possible image processing procedures to enhance the result in brain image analysis.

A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians

https://repositorio-aberto.up.pt/bitstream/10216/122067/2/348986.1.pdf

K. Arvind Karthik

Papers

A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality