Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations.

This dataset relates to 'Deep Learning Binomial Expansions' paper. A Computer Vision model with 5 concurrent Softmax layers identifies the coefficients, exponent and whether X and Y coefficients are positive or negative.

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.