Machine Learning Model Deployment
Deployment Date: 2022-09-05 08:48:24 UTC
Artificial Intelligence |
Statistical Modeling |
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Python |
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Banking and Finance | Health Care | Telecommunications |
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Category: Domain Usecases
Sub-Category: Health Care and Pharmaceuticals
Use-case Type: Image Classification
Private API
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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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