Algorithm 1: The Proposed CAD Algorithm
1	Input → TB Chest X-ray Dataset TBD.
2	Output ← Fine-tuned CAD Model using ViT TB diagnosis.
3	BEGIN
4	STEP 1: Pre-processing of Images
5	FOR EACH CXR image IN the TBD DO
6	Remove noise.
7	Resize 224 × 224 pixels.
8	Scaling pixel to range [0, 1].
9	END FOR
10	STEP 2: Splitting TBD
11	SPLIT TBD INTO
12	Training set → 80% of TBD.
13	Test set → 20% of TBD.
14	STEP 3: Six Models Pre-training
15	FOR EACH DL IN [ViT, DenseNet121, MobileNet, ResNet101V2, Xception] DO
16	Load DL.
17	Pre-train DL on the ImageNet dataset.
18	Extract deep features from CXR images.
19	END FOR
20	STEP 4: Five ML Models Fine-Tunning
21	FOR EACH ML IN [RF, XGB, DT, SVM, and AdaBoost] DO
22	Fine-tune ML on the training set.
23	END FOR
24	STEP 5: Model Evaluation
25	FOR EACH ML IN [RF, XGB, DT, SVM, and AdaBoost] DO
26	Evaluate the effectiveness of ML on the test set of the fine-tuned CAD model.
27	END FOR
28	Keep the accuracy of the fine-tuned CAD model.
29	END