abhisheks008 · devarshh08 · Jun 8, 2024
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+The link for the dataset used in this project:  https://www.kaggle.com/datasets/shashwatwork/knee-osteoarthritis-dataset-with-severity/data
+
+The dataset consists of four subdirectories: train, test, auto test and val (test and auto_test are the same). All four contain 5 subdirectories, each representing a severity of osteoarthritis. The test and auto_test subdirectory contains 1346 images in total, while the train subdirectory contains 5778 images in total and the val subdirectory contains 826 images in total.
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diff --git a/Knee Osteoarthritis Prediction/Readme.md b/Knee Osteoarthritis Prediction/Readme.md
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+## **Knee Osteoarthritis Prediction**
+
+### 🎯 **Goal**
+
+The objective of this project is to classify xrays of Knee Xrays into 5 distinct categories based on severity of the disease.
+
+### 🧵 **Dataset**
+
+The dataset consists of four subdirectories: train, test, auto test and val (test and auto_test are the same). All four contain 5 subdirectories, each representing a severity of osteoarthritis. The test and auto_test subdirectory contains 1346 images in total, while the train subdirectory contains 5778 images in total and the val subdirectory contains 826 images in total.
+
+### 🧾 **Description**
+
+The project deals with multi-class classification, classifying images into 5 grades of severity.
+
+### 🧮 **What I had done!**
+
+To achieve our goals, the following steps were implemented:
+
+- Images were loaded using keras.utils and normalized to the range 0 to 1.
+
+- Images were resized to a fixed size of 224x224 pixels.
+
+- Custom and pre-trained models were used for this task.
+
+### 🚀 **Models Implemented**
+
+models used:
+
+- ResNet-50
+- Xception
+- VGG16
+- CNN-Keras
+- InceptionV3
+- DenseNet-121
+- EfficientNetB7
+
+### 📚 **Libraries Needed**
+
+- Keras
+
+- Tensorflow
+
+- Numpy
+
+- Matplotlib
+
+We used Validation and Testing **Loss** and **Accuracy** as metrics.
+
+| Models | Accuracy | Loss |
+|--------|---------------------|--------------------------|
+| ResNet-50 | % |  |
+| InceptionV3 | %  |  | 
+| CNN | % |  |
+| VGG16 | % |  |
+| EfficientNetB7 | % |  |
+| DenseNet-121 | % |  |
+| Xception | % |  |
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		The link for the dataset used in this project: https://www.kaggle.com/datasets/shashwatwork/knee-osteoarthritis-dataset-with-severity/data

		The dataset consists of four subdirectories: train, test, auto test and val (test and auto_test are the same). All four contain 5 subdirectories, each representing a severity of osteoarthritis. The test and auto_test subdirectory contains 1346 images in total, while the train subdirectory contains 5778 images in total and the val subdirectory contains 826 images in total.