Next Article in Journal
A New Ground-Based Pseudolite System Deployment Algorithm Based on MOPSO
Previous Article in Journal
A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion
Previous Article in Special Issue
AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task

Transfer Learning Approach for Classification of Histopathology Whole Slide Images

Department of Computer Science and Information Technology, University of Balochistan, Quetta 87300, Pakistan
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Author to whom correspondence should be addressed.
Academic Editor: Alessandro Artusi
Sensors 2021, 21(16), 5361;
Received: 25 June 2021 / Revised: 6 August 2021 / Accepted: 7 August 2021 / Published: 9 August 2021
The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79. View Full-Text
Keywords: deep learning; transfer learning; histopathology deep learning; transfer learning; histopathology
Show Figures

Graphical abstract

MDPI and ACS Style

Ahmed, S.; Shaikh, A.; Alshahrani, H.; Alghamdi, A.; Alrizq, M.; Baber, J.; Bakhtyar, M. Transfer Learning Approach for Classification of Histopathology Whole Slide Images. Sensors 2021, 21, 5361.

AMA Style

Ahmed S, Shaikh A, Alshahrani H, Alghamdi A, Alrizq M, Baber J, Bakhtyar M. Transfer Learning Approach for Classification of Histopathology Whole Slide Images. Sensors. 2021; 21(16):5361.

Chicago/Turabian Style

Ahmed, Shakil, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber, and Maheen Bakhtyar. 2021. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images" Sensors 21, no. 16: 5361.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop