Image Processing

The Brain Tumor Detection Dataset is a dataset that's specifically designed for detecting brain tumours using advanced computer vision techniques. It's compatible with YOLOv8 an efficient and real-time object detection algorithm. The dataset was last updated about a year ago and is curated to help accurately detect and classify brain tumours into three distinct classes. The main goal of the project is to contribute to the early detection and diagnosis of brain tumours, which aims to provide valuable support to medical professionals in creating effective treatment plans.

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients.

RE-Flare has 7,680 images, including 1,820 triplets of flare-corrupted, flare-free, and low-light images captured by conventional cameras. Specifically, to tackle the challenge of accurately estimating flare location and shape, we additionally offer 1,110 pairs of light masks and light-source edge masks based on the event stream, providing diverse choices for data-driven implementations.

LIVE-Viasat Real-World Satellite QoE Database contains 179 videos from real-world streaming, encompassing a range of distortions. Enhanced by a study with 54 participants providing detailed QoE feedback, our work not only provides a rich analysis of the determinants of subjective QoE but also delves into how various streaming impairments influence user behavior, thereby offering a more holistic understanding of user satisfaction.