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Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

100%

Hegde R. B. , Prasad K. , Hebbar H. , Singh B. M. K.

Biocybernetics and Biomedical Engineering

2019

tom Vol. 39, no. 2

382--392

Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.

Multi-organ squamous cell carcinoma classification using feature interpretation technique for explainability

88%

Prabhu S. , Prasad K. , Hoang T. , Lu X. , Sandhya I.

Biocybernetics and Biomedical Engineering

2024

tom Vol. 44, no. 2

312--326

Squamous cell carcinoma is the most common type of cancer that occurs in many organs of the human body. To detect carcinoma, pathologists observe tissue samples at multiple magnifications, which is time-consuming and prone to inter- or intra-observer variability. The key challenge for automation of squamous cell carcinoma diagnosis is to extract the features at low (100x) magnification and explain the decision-making process to healthcare professionals. The existing literature used either machine learning or deep learning models to detect squamous cell carcinoma of specific organs. In this work, we report on the implementation of an explainable diagnostic aid system for squamous cell carcinoma of any organ and present a comparative analysis with state-of-the-art models. A classifier with an ensemble feature selection technique is developed to provide an automatic diagnostic aid for distinguishing between squamous cell carcinoma positive and negative cases based on histopathological images. Moreover, explainable AI techniques such as ELI5, LIME and SHAP are introduced to machine learning model which provides feature interpretability of prediction made by the classifier. The results show that the machine learning model achieved an accuracy of 93.43% and 96.66% on public and multi-centric private datasets, respectively. The proposed CatBoost classifier achieved remarkable performance in diagnosing multi-organ squamous cell carcinoma from low magnification histopathological images, even when various illumination variations were introduced.