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12 results on '"Michael J Horry"'

3. Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models

Author

Michael J. Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Jing Zhu, Hui Wen Loh, Prabal Datta Barua, and U. Rajendra Acharya

Subjects
chest X-ray, confounding bias, deep learning, model generalization, lung cancer, federated learning, Chemical technology, TP1-1185
Abstract

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Published
2023
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11. Features Of ICU Admission In X-Ray Images Of Covid-19 Patients

Author

Manash Saha, Michael J. Horry, Douglas Pinto Sampaio Gomes, Tanmoy Debnath, Anwaar Ulhaq, D. M. Motiur Rahaman, Manoranjan Paul, and Subrata Chakraborty

Subjects
medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), business.industry, medicine, X ray image, Radiology, business, Icu admission
Published
2021
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12. Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

Author

Michael J Horry, Subrata Chakraborty, Biswajeet Pradhan, maryam fallahpoor, Chegeni Hossein, and Manoranjan Paul

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, engrXiv|Engineering|Biomedical Engineering and Bioengineering, bepress|Engineering, engrXiv|Engineering|Computer Engineering, engrXiv|Engineering|Computer Engineering|Other Computer Engineering, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, bepress|Engineering|Biomedical Engineering and Bioengineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG), bepress|Engineering|Computer Engineering|Other Computer Engineering, engrXiv|Engineering, FOS: Electrical engineering, electronic engineering, information engineering, bepress|Engineering|Computer Engineering
Abstract

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications., 39 Pages, 8 figures, 14 tables comparing the generalization of COVID-19 CT Deep Learning Models

Published
2021

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