Explanation All Code

Step 1:

Mounting Google Drive

We mount Google Drive to access our dataset stored in the cloud.

Importing Libraries

We import essential libraries for data processing, model building, evaluation, and visualization.

Data collection and preparation:

We utilized the cervical cancer dataset for Deep learning classification. We collected a cervical cancer dataset consisting of 5000 images. After augmenting the images, the total dataset increased to 7000 images. Next, divide the dataset into 80% for training and 20% for validation.

Load Datasets

We set the paths for training and validation datasets stored in Google Drive.

Listing Categories

We list the categories (labels) in the training dataset directory.

Step 2:

Data Processing

We define a function to read and resize images, storing them with their respective labels. We also count the number of images per class.

We process the training data and print the total number of training samples.

Plotting Class Distributions

We plot the distribution of classes in the training dataset.

Processing Validation Data

We process the validation data and print the total number of validation samples.

Plotting Validation Data Distribution

We plot the distribution of classes in the validation dataset.

Step 3:

Preparing Data for Model

We convert image data and labels to numpy arrays and normalize the images.

Visualizing Random Training Images

We display random images from the training dataset for visualization.

Step 4:

Cervical Cancer Detection Using CNN, EfficientNetB0, and Sequential Model

• Basic CNN (Convolutional Neural Network): This model is designed to recognize patterns in images, making it a strong choice for image classification tasks like detecting cervical cancer cells.
• EfficientNetB0: This is a state-of-the-art model known for its efficiency and accuracy. It balances between performance and resource usage, making it ideal for complex image classification tasks.
• Sequential Model: This model allows for simple building and training of neural networks layer by layer, providing flexibility in designing a custom architecture for cervical cancer detection.

By using these models, my project aims to accurately classify cervical cancer from medical images, leveraging the strengths of each model to improve overall performance.

Building a Basic CNN Model

We define and compile a basic CNN model.

Training the Basic CNN Model

We train the model and save the best weights.

Plotting Training History

We plot the accuracy and loss curves of the model.

Evaluating the Model

We load the best model and evaluate it on the test data. There is the accuracy of 89.50%.

Plotting Confusion Matrix and Classification Report

Step 5:

Using EfficientNetB0

Building a EfficientNetB0

We define and compile a EfficientNetB0 model.

We train the model and save the best weights.

We save the EfficientNet model architecture and weights.

Plotting Training History

We plot the accuracy and loss curves of the model.

Evaluating the Model

We plot the confusion matrix and print the classification report.

Step 6:

We define and compile a Sequential Model.

Training the Sequential Model

We train the model and save the best weights.

We plot the accuracy and loss curves of the model.

We load the best model and evaluate it on the test data. There is Sequential Model Accuracy: 91.29%

Plotting Confusion Matrix and Classification Report

We plot the confusion matrix and print the classification report.

Step 7:

Prediction

Conclusion

In conclusion, our project highlights the potential of deep learning in enhancing cervical cancer detection. By employing models like EfficientNet and basic CNN architectures, we achieved promising results in classifying cervical cell images. These automated systems can significantly support healthcare professionals in early diagnosis, leading to better patient outcomes. Future improvements could include expanding the dataset, refining the models, and developing a user-friendly application for clinical use, further bridging the gap between technology and healthcare.