Table 2 Pseudocode for local server model training and global server model aggregation.
Steps | Description |
|---|---|
1 | Start |
2 | Collect lung cancer data (CT scans, X-rays) from the dataset83 |
3 | Perform data preprocessing: • Histogram • Noise Reduction • Edge Detection • Color Space Transmission |
4 | Split data into Training Dataset (70%) and Testing Dataset (30%). |
5 | Apply MapReduce for distributed processing: • Map Phase: Divide training data into smaller chunks and process locally. • Shuffle Phase: Group intermediate results by key. • Reduce Phase: Aggregate the processed data into meaningful outputs. |
6 | Store the preprocessed Training Dataset and Testing Dataset securely using Private Blockchain Technology to ensure data integrity and secure sharing. |
7 | Initialization layer weights \(\:{m}_{ij}\) & \(\:{u}_{jk}\)), Error, \(\:E=\:0\), and the number of epochs \(\epsilon=0\) |
8 | For each training pattern, \(\:p\), do: |
9 | a. Feedforward phase (calculate hidden layer error): Calculate \(\:{{\Delta\:}}_{j}\) using the equation: \(\:{{\Delta\:}}_{j}=\:{f{\prime\:}(net}_{j})\times\:{\sum\:}_{k}{w}_{jk}{{\Delta\:}}_{k}\) (8) Calculate \(\:{{\Delta\:}}_{j}\) using the equation: \(\:{{\Delta\:}}_{k}=\:{f{\prime\:}(net}_{k})\times\:{(target}_{k}-{output}_{k})\) (9) |
10 | b. Backpropagation (update weights using): \(\:{{\Delta\:}u}_{jk}=\eta\:{{\Delta\:}}_{k}{y}_{j}\) (10) \(\:{{\Delta\:}m}_{ij}=\eta\:{{\Delta\:}}_{j}{x}_{i}\) (11) |
11 | Increment the epoch count: \(\epsilon=\epsilon+1\) |
12 | Check Learning Rate: If the desired learning rate is achieved, store the trained model in the Cloud System. If not, retrain the model using local data. |
13 | Send the optimized local model weights \(\:{m}_{ij}\) and \(\:{u}_{jk}\) to the Global Cloud for model aggregation. |
14 | Perform Global Model Aggregation in the cloud using inputs from all local models. |
15 | Synchronize the aggregated global model back to local nodes. |
16 | Validation Phase: • Import the trained global model from the cloud. • Use the Testing Dataset to predict lung cancer. |
17 | If lung cancer is predicted, display a “Lung Cancer Found” message. If not, discard the results or refine the model. |
18 | Stop |
