Pneumonia Disease Classification | Machine Learning

Zeeshan Akram

Performed Steps:

Got data from Kaggle public datasets.
Configured TPU (Tensor Processing Unit).
Performed preprocessing and image augmentation on data.
Prepared data for training and validation.
Build a model using transfer learning.
Train and validate the model.
Made predictions.

Project Details:

Python Version: 3.7
Packages: numpy, pandas, kaggle_datasets, tensorflow, keras, matplotlib, efficientnet, sklearn, seaborn, re, math, cv2, PIL, os.
Model: EfficientNet B6 (Transfer Learning with Imagenet weights).
Training Result:
Recall: 0.9542
Precision: 0.9881
Accuracy: 0.9575
Validation Results:
Recall: 0.9744
Precision: 0.9870
Accuracy: 0.9713
Predictions Accuracy: 88.30%

Code Previews:

For full source code please visit the GitHub repository.

Github Repository Link:

Like this project
0

Posted Jul 23, 2024

I created this model to classify pneumonia disease by using the transfer learning technique with the EfficientNet B6 model.

Autonomous HVAC Control | Machine Learning
Autonomous HVAC Control | Machine Learning
Anomaly Detection System | Artificial Intelligence
Anomaly Detection System | Artificial Intelligence
Predict FIFA Players Value | Machine Learning
Predict FIFA Players Value | Machine Learning