Added Leukaemia Classification using DL

Algorithms and Deep Learning Models/Leukaemia Classification using DL/Dataset.md

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+The link for the dataset used in this project: https://www.kaggle.com/datasets/andrewmvd/leukemia-classification

Algorithms and Deep Learning Models/Leukaemia Classification using DL/README.md

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+# Leukaemia Classification using DL
+
+## PROJECT TITLE
+
+Leukaemia Classification using DL
+
+## 🎯 GOAL
+
+To classify normal from abnormal cell images of Leukaemia.
+
+## 🧵 DATASET
+
+The link for the dataset used in this project: https://www.kaggle.com/datasets/andrewmvd/leukemia-classification
+
+## 🧾 DESCRIPTION
+
+This project aims to identify whether the given medical image contains Leukaemia cells or not.
+
+## 🧮 WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above. 
+
+2. Data preprocessing: Preprocessed the image in order to have all images in equal shape.
+
+3. Model selection: Chose three Image detection architecture VGG16, ResNet50 and Inception for Image detection. Created models for CNN and CNN with Attention mechanism.
+
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+## 🚀 MODELS USED
+
+1. VGG16
+2. ResNet50
+3. Inception
+4. Xception
+5. CNN
+6. CNN with Attention
+
+## 📚 LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- numpy==1.24.3
+- pandas==1.5.0
+- matplotlib==3.6.0
+- tensorflow==2.6.0
+
+## 📋 EVALUATION METRICS
+
+The evaluation metrics used for assessing the models:
+
+- Accuracy 
+- Loss
+
+## 📈 RESULTS
+
+Results on Val dataset:
+
+| Model      | Accuracy | Loss    |
+|------------|----------|---------|
+| Inception    | 0.775     | 0.498   |
+| ResNet50    | 0.802     | 0.514    |
+| VGG16    | 0.77     | 0.536    |
+| CNN    | 0.784     | 0.506    |
+| Xception    | 0.783     | 0.533    |
+| CNN (Attention)    | 0.858     | 0.39    |
+
+## 📢 CONCLUSION
+
+Based on results we can draw following conclusions:
+
+1. Inception: The Inception model achieved an accuracy of 77.5% with a loss of 0.498. It demonstrates good performance in distinguishing between leukemia and non-leukemia samples.
+
+2. ResNet50: The ResNet50 model performed slightly better with an accuracy of 80.2% and a loss of 0.514. It shows improved capabilities compared to Inception in leukemia detection.
+
+3. VGG16: The VGG16 model achieved an accuracy of 77.0% with a loss of 0.536. It falls slightly behind ResNet50 in terms of accuracy and loss.
+
+4. CNN: The CNN model achieved an accuracy of 78.4% with a loss of 0.506. It demonstrates similar performance to ResNet50 and shows potential in leukemia detection.
+
+5. Xception: The Xception model achieved an accuracy of 78.3% with a loss of 0.533. It shows comparable performance to the other models in this task.
+
+6. CNN with Attention: The CNN with Attention mechanism model achieved an astonishingly high accuracy of 85.8% with a significantly lower loss of 0.39. It demonstrates exceptional abilities to generalize and classify with a simple and lightweight architecture.
+
+Overall, all the models performed relatively well in leukemia detection, with accuracies ranging from 77% to 85.8%, with CNN-Attention being the clear winner.  
+

Algorithms and Deep Learning Models/Leukaemia Classification using DL/Requirements.txt

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+numpy==1.24.3
+pandas==1.5.0
+matplotlib==3.6.0
+tensorflow==2.6.0
+Keras