A survey on cancer detection via convolutional neural networks: Current challenges and future directions
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT
Lung cancer is one of the most fatal malignant diseases, which poses an acute menace to
human health and life. The accurate differential diagnosis of lung nodules is a vital step in …
human health and life. The accurate differential diagnosis of lung nodules is a vital step in …
Effective lung nodule detection using deep CNN with dual attention mechanisms
Z UrRehman, Y Qiang, L Wang, Y Shi, Q Yang… - Scientific Reports, 2024 - nature.com
Novel methods are required to enhance lung cancer detection, which has overtaken other
cancer-related causes of death as the major cause of cancer-related mortality. Radiologists …
cancer-related causes of death as the major cause of cancer-related mortality. Radiologists …
[HTML][HTML] MITER: Medical Image–TExt joint adaptive pretRaining with multi-level contrastive learning
Recently multimodal medical pretraining models play a significant role in automatic medical
image and text analysis that has wide social and economical impact in healthcare. Despite …
image and text analysis that has wide social and economical impact in healthcare. Despite …
Ensemble framework based on attributes and deep features for benign-malignant classification of lung nodule
J Qiao, Y Fan, M Zhang, K Fang, D Li… - … Signal Processing and …, 2023 - Elsevier
Early detection and identification of malignant lung nodules improve the survival of lung
cancer patients. The visual attributes such as subtlety, spiculation, and calcification of lung …
cancer patients. The visual attributes such as subtlety, spiculation, and calcification of lung …
Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features
Lung cancer has the highest incidence in the world. The standard tests for its diagnostics are
medical imaging exams, sputum cytology, and lung biopsy. Computed Tomography (CT) of …
medical imaging exams, sputum cytology, and lung biopsy. Computed Tomography (CT) of …
An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules
SB Shuvo - arXiv preprint arXiv:2305.00046, 2023 - arxiv.org
Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is
crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major …
crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major …
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound
Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …
representations for transfer learning, leveraging large amounts of unlabelled data. This …
[HTML][HTML] Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤ 10 mm) solid pulmonary nodules
J Liu, L Qi, Y Wang, F Li, J Chen, S Cheng… - Journal of Thoracic …, 2023 - ncbi.nlm.nih.gov
Background This study assessed the diagnostic performance of a deep learning (DL)-based
model for differentiating malignant subcentimeter (≤ 10 mm) solid pulmonary nodules …
model for differentiating malignant subcentimeter (≤ 10 mm) solid pulmonary nodules …