_Colon Detection mini project ppt.pptx..

Colour detection

1. Gesture-Driven Colon Detection Toolkit for Doctors using Efficient Net & ResNet By : Sanjiv S Kishore Harshan Kumar R Mini Project

2. Problem Statement 1.Global Challenge: Colon ailments pose a significant global health concern. Precise and timely diagnosis is vital for effective treatment. 2.Manual Interpretation Issues: Current methods rely on manual interpretation, leading to time-consuming and subjective processes. There's a clear need for more efficient and automated diagnostic approaches. 3.Project Objective: Develop a Gesture-Driven Colon Détection Toolkit. Aiming to revolutionize and automate colon ailment diagnostics for improved efficiency and accuracy.

3. Algorithms Used Rule-Based Recommendation MediaPipe Efficient Net ResNet Semantic Segmentation UNet

4. The "normal" class serves as a detracting reference point, representing healthy colon fabric devoid of abnormalities. Including these images enriches dataset diversity , providing essential guideline for training diagnostic models. Image processing focuses on mucosal patterns , constant structure , establishing benchmarks for normalcy. Model Classes Normal: Polyps : Ulcerative colitis : IBD Category: Part of inflammatory bowel disease (IBD). Distinct Traits : Manifests in the colon with inflammation and ulcers, displaying unique visual patterns in colonoscopy images. Common Colonic Incident: Polyps, a prevalent incident in the colon, involves tissue growth inside the colon interlining. Diversity in Visual Presences: Polyps exhibit varied visual characteristics in colonoscopy, ranging from small growths to potentially precancerous lesions. Esophagitis: Inflammatory Condition: Esophagitis involves esophageal inflammation. Distinctive Patterns: It exhibits characteristic optic patterns in endoscopic images.

5. Dataset :

6. A. Arm Robot B. Endoscopic Camera Hardware Integration : Real-time Precision :Crucial for surgeries, these cameras capture precise real-time data. Colon Detection & Navigation: They efficiently navigate and detect the colon, ensuring feasibility with flexible movements. Enhanced Visualization: Advanced capabilities offer clearer visuals of the colon's layers, aiding in the detection of issues like polyps and ulcers. Precision Movements: Utilizes servo motors for accurate joint movements, replicating human arm precision during surgery. End-Effector Functionality :Equipped with a specific tool holder at the arm's terminal section, aiding in precise tool guidance during procedures. Communication via PyFirmata: Arduino board, interfaced with PyFirmata in Python, serves as a communication bridge for command transmission.

7. Main Workflow

8. Effective Pattern Recognition with Neural Networks STRENGTH WEAKNESS Limited Model Interpretability Revolutionizing Non-intrusive Healthcare Interpretation OPPORTUNITY THREATS Risk of Over-Reliance on AI Predictions SWOT Analysis