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@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.ipynb_checkpoints/class_distribution-checkpoint.png filter=lfs diff=lfs merge=lfs -text
+.ipynb_checkpoints/roc_curves-checkpoint.png filter=lfs diff=lfs merge=lfs -text
+.ipynb_checkpoints/sample_predictions-checkpoint.png filter=lfs diff=lfs merge=lfs -text
+attention_weights.png filter=lfs diff=lfs merge=lfs -text
+class_distribution.png filter=lfs diff=lfs merge=lfs -text
+confusion_matrices.png filter=lfs diff=lfs merge=lfs -text
+model_comparison.png filter=lfs diff=lfs merge=lfs -text
+roc_curves.png filter=lfs diff=lfs merge=lfs -text
+sample_predictions.png filter=lfs diff=lfs merge=lfs -text
+training_history.png filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/results_summary-checkpoint.json

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detailed_report.txt

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+LEUKEMIA CLASSIFICATION ENSEMBLE MODEL REPORT
+==================================================
+
+Generated on: 2025-05-22 13:06:19
+
+CONFIGURATION:
+--------------------
+ORIGINAL_DIR: ./Original
+SEGMENTED_DIR: ./Segmented
+BATCH_SIZE: 32
+NUM_EPOCHS: 100
+LEARNING_RATE: 0.001
+PATIENCE: 15
+CLASS_NAMES: ['Benign', 'Early', 'Pre', 'Pro']
+NUM_CLASSES: 4
+IMG_SIZE: 224
+RANDOM_STATE: 42
+
+MODEL PERFORMANCE SUMMARY:
+------------------------------
+
+RESNET50:
+  Overall Accuracy: 0.9816
+  Per-class Performance:
+    Benign:
+      Precision: 0.9412
+      Recall: 0.9600
+      F1-Score: 0.9505
+    Early:
+      Precision: 0.9798
+      Recall: 0.9798
+      F1-Score: 0.9798
+    Pre:
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+      Recall: 0.9896
+      F1-Score: 0.9896
+    Pro:
+      Precision: 1.0000
+      Recall: 0.9877
+      F1-Score: 0.9938
+  Macro Avg F1-Score: 0.9784
+  Weighted Avg F1-Score: 0.9817
+
+ENSEMBLE:
+  Overall Accuracy: 0.9785
+  Per-class Performance:
+    Benign:
+      Precision: 0.9783
+      Recall: 0.9000
+      F1-Score: 0.9375
+    Early:
+      Precision: 0.9519
+      Recall: 1.0000
+      F1-Score: 0.9754
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+    Pro:
+      Precision: 1.0000
+      Recall: 0.9877
+      F1-Score: 0.9938
+  Macro Avg F1-Score: 0.9741
+  Weighted Avg F1-Score: 0.9783
+
+UNET:
+  Overall Accuracy: 0.9755
+  Per-class Performance:
+    Benign:
+      Precision: 0.9388
+      Recall: 0.9200
+      F1-Score: 0.9293
+    Early:
+      Precision: 0.9612
+      Recall: 1.0000
+      F1-Score: 0.9802
+    Pre:
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+      Recall: 0.9896
+      F1-Score: 0.9948
+    Pro:
+      Precision: 0.9873
+      Recall: 0.9630
+      F1-Score: 0.9750
+  Macro Avg F1-Score: 0.9698
+  Weighted Avg F1-Score: 0.9754
+
+EFFICIENTNET:
+  Overall Accuracy: 0.9693
+  Per-class Performance:
+    Benign:
+      Precision: 0.9362
+      Recall: 0.8800
+      F1-Score: 0.9072
+    Early:
+      Precision: 0.9510
+      Recall: 0.9798
+      F1-Score: 0.9652
+    Pre:
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+      Recall: 1.0000
+      F1-Score: 0.9897
+    Pro:
+      Precision: 1.0000
+      Recall: 0.9753
+      F1-Score: 0.9875
+  Macro Avg F1-Score: 0.9624
+  Weighted Avg F1-Score: 0.9691
+
+VGG19:
+  Overall Accuracy: 0.9417
+  Per-class Performance:
+    Benign:
+      Precision: 0.9286
+      Recall: 0.7800
+      F1-Score: 0.8478
+    Early:
+      Precision: 0.8981
+      Recall: 0.9798
+      F1-Score: 0.9372
+    Pre:
+      Precision: 0.9495
+      Recall: 0.9792
+      F1-Score: 0.9641
+    Pro:
+      Precision: 1.0000
+      Recall: 0.9506
+      F1-Score: 0.9747
+  Macro Avg F1-Score: 0.9310
+  Weighted Avg F1-Score: 0.9407
+
+VIT:
+  Overall Accuracy: 0.9172
+  Per-class Performance:
+    Benign:
+      Precision: 0.8750
+      Recall: 0.5600
+      F1-Score: 0.6829
+    Early:
+      Precision: 0.8319
+      Recall: 1.0000
+      F1-Score: 0.9083
+    Pre:
+      Precision: 0.9694
+      Recall: 0.9896
+      F1-Score: 0.9794
+    Pro:
+      Precision: 1.0000
+      Recall: 0.9506
+      F1-Score: 0.9747
+  Macro Avg F1-Score: 0.8863
+  Weighted Avg F1-Score: 0.9111
+
+Best performing model: ResNet50 with 0.9816 accuracy

efficientnet_model.pth

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results_summary.json

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