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An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for
Silambarasi Palanivel1, Viswanathan Nallasamy2
1Department of Electronics and Communication Engineering, Mahendra Engineering College for Women, Tamil Nadu, India.
Insights
This study introduces an Inception ResNet-v3 model for automated white blood cell (WBC) classification, achieving high accuracy. This AI-driven approach offers a promising tool for improving clinical blood examination diagnostics.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Hematology
Background:
- White blood cell (WBC) classification is crucial for diagnosing various medical conditions, including infections and leukemia.
- Traditional methods often involve manual analysis, which can be time-consuming and prone to error.
- Machine learning (ML) offers automated solutions for WBC classification, improving efficiency and accuracy.
Purpose of the Study:
- To develop and evaluate an automated WBC classification system using deep learning.
- To improve the accuracy and efficiency of diagnosing blood disorders through AI.
Main Methods:
- The study proposes an Inception ResNet-v3 model, integrating Inception architecture and ResNet connections.
- The model was trained on a dataset of 15.7k human peripheral WBC images across five categories.
- Pathologist-verified diagnoses were used for training the classification model.
Main Results:
- The Inception ResNet-v3 model achieved high accuracy in classifying five types of WBCs.
- Performance was superior to existing models like VGG, U-Net, and ResNet.
- Testing on public datasets (Kaagel, Raabin) yielded accuracies of 98.80% and 98.95%, respectively.
Conclusions:
- The proposed Inception ResNet-v3 model demonstrates significant potential for enhancing clinical blood examination diagnostics.
- It offers a promising, accurate, and efficient alternative to traditional ML methods for WBC classification.
- The model achieved excellent performance metrics, including Accuracy, Precision, Recall, Specificity, and F1 Score.
Objectives:
The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.
Methods:
The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.
Results:
After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.
Conclusions:
Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.
