Publications

Deep Learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey - 2023 -Link 

(Status: Submitted to the Multimedia Tools and Applications Journal of Springer)

Abstract

Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA volume requires a radiologist, which is time-consuming and error-prone due to the specific conditions of lung tissue, large volume of data, lack of experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are used as a second opinion for the diagnosis of PE. The purpose of this article is to review, evaluate, and compare the performance of deep learning and traditional-based CAD system for diagnosis PE and to help physicians and researchers in this field. In this study, all articles available in databases such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the field of PE diagnosis were examined using traditional and deep learning methods. From 2002 to 2023, 23 papers were studied to extract the articles with the considered limitations. Each paper presents an automatic PE detection system that we evaluate using criteria such as sensitivity, False Positives (FP), and the number of datasets. This research work includes recent studies, state-of-the-art research works, and a more comprehensive overview compared to previously published review articles in this research area.

A New Real-Time Method for Lung Lesion-Mass Detection in CT Images - 2023 - LINK

(Status: Submitted to the Computer Methods in Biomechanics and Biomedical Engineering)

Abstract

Detection of lung mass lesion is widely required in the clinic for diagnosis of different pulmonary defects like lung cancer and pulmonary embolism. Due to severe symptoms of such diseases, real-time detection of the lung mass lesion is significant. In this paper, this problem is addressed by a new image segmentation algorithm. Primarily, a Gabor-based filtering algorithm is employed to remove the intensity nonuniformity of the CT image. Then, the holes and discontinuities of the mass lesion are eliminated through morphological operations. Finally, the lesion objects are extracted by using the thresholding and connected-component-analysis. The experimental results demonstrated significantly short CPU time of the proposed algorithm, as 73 milliseconds for each slice on a typical laptop. Also, the solution quality of our method was considerably high, as 91.6%, 85.4%, and 93.3% in terms of accuracy, sensitivity, and specificity, respectively. We further showed that the proposed method provided better solutions compared to six other counterpart algorithms.

Deep Learning Applications for Lung Cancer Diagnosis: A systematic review - 2023- LINK

Abstract

Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researchers in this field. The main purpose of this work is to identify the challenges that exist in lung cancer based on deep learning. The survey is systematically written that combines regular mapping and literature review to review 32 conference and journal articles in the field from 2016 to 2021. In this work, after a complete analysis and review of the articles, the questions raised in the articles have been answered. This research work provides a more comprehensive review compared to previous published review articles in this research area. Furthermore, it includes recent studies and state of the art research works systematically.

Keywords: lung cancer detection, deep learning, systematic survey;

The authors would like to thank you Dr. Armin Chitizade for reviewing the manuscript and Dr. Ghasem Naghib for general guidance through this research.