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AI and Deep Learning for Ear Infection Detection ~xRay Pixy

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Learn how AI and deep learning revolutionize ear infection detection, enabling accurate, fast, and automated diagnosis using advanced image processing and machine learning techniques. Video Chapters: Ear Infection Detection using AI and DL 00:00 Introduction 00:14 My Experience with Ear Infections 01:15 Topics Covered 02:24 Ear Infections 02:48 Ear Infection Signs 03:55 Ear Infection Preventions 04:29 Ear Infection Types 05:19 Ear Infection Causes 06:14 How Bacteria and Fungus Grow in Ear 07:26 My Mistakes 08:49 Doctors Advise after Ear Infection 09:45 Ear Infection Common Symptoms 10:37 Automated Ear Infection Detection with Deep Learning AI 15:09 Smartphone Otoscopes 16:04 Conclusion Ear fungus, also known as otomycosis , is a fungal infection of the outer ear canal. If an ear infection is not treated on time, it can lead to serious complications.  Hearing Loss – Persistent infections can damage the eardrum and middle ear structures, leading to partial or permanent hearing loss....
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What is the difference between local descriptors and global descriptors?

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Descriptors rely on Gradient-based or intensity variation approaches to detect Local Features (e.g., Edges, Blobs, and Regions).
BLOB = Binary Large Object (i.e., the region of the image).
Descriptors such as HOG, SIFT, SURF (rely on local gradient computation).
Binary Descriptors such as BRISK, ORB or FREAK (rely on local intensity differences)
Global Descriptor – Describe the whole image,
Local Descriptor – Describe the patch within the image. (The best example of the global descriptor is SIFT).
LDP = Local Directional Pattern.
Local Directional Pattern is a descriptor i.e., used for Face Recognition.
How to Calculate Local Directional Pattern (LDP) Code?|Kirsch Compass Mask|
https://youtu.be/dY0KarfWTV4

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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