Machine Learning Model Deployment

Deployment Date: 2022-09-05 08:48:24 UTC


Vaibhav Mali

Data Science Skills

Artificial Intelligence

Statistical Modeling

Text Analytics

Big Data

Digital Analytics

Visualization

Python

R

Tableau

Domain Skills

Banking and Finance

Health Care

Telecommunications

Retail

Insurance

Classification of gastrointestinal abnormalities by endoscopic imaging with deep learning


Category: Domain Usecases

Sub-Category: Health Care and Pharmaceuticals

Use-case Type: Image Classification

Tags: endoscopy,gastrointestinal_disease,healthcare,small_intenstine,surgery

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Use Case Summary

Gastrointestinal Endoscopydeals with the endoscopic examination, therapy or surgery of the gastrointestinal tract. Gastrointestinal Tract generally refers to the digestive structures stretching from the mouth to anus, but does not include the accessory glandular organs such as liver, billary tract and panceras.

Currently, the most commonly used imaging methods for detection of gastrointestinal disorders, including disorders of the small intestine, are endoscopy and radiological imaging has made it possible to diagnose thegastrointestinaldiseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system. The extraction of features plays a key role in helping toGastrointestinal Endoscopy Diseases.

We proposed an image processing-based method to detectGastrointenstinal diseases. This method takes the digital image of disease effect intenstinalarea, then use image analysis to identify the type of disease.

Description Of Dataset:

The data consists of images of8 types of Gastrointestinal diseases.The total number of images are around 4000, out of which approximately3200 have been split in the training set and the remaining in the test set.

Source Of Dataset:-

https://www.kaggle.com/datasets/meetnagadia/kvasir-dataset

Model Used: Transfer Learning (Vgg19), ANN
Accuracy : 85.00%.









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