Deep learning techniques to diagnose lung cancer
L Wang - Cancers, 2022 - mdpi.com
Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …
research directions of deep learning techniques for lung cancer and pulmonary nodule …
Computer-aided diagnosis of coal workers' pneumoconiosis in chest x-ray radiographs using machine learning: A systematic literature review
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers'
pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly …
pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly …
LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation
In this paper, we propose a novel Low-cost U-Net (LCU-Net) for the Environmental
Microorganism (EM) image segmentation task to assist microbiologists in detecting and …
Microorganism (EM) image segmentation task to assist microbiologists in detecting and …
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public
health emergency globally. The number of infected people and deaths are proliferating …
health emergency globally. The number of infected people and deaths are proliferating …
[HTML][HTML] An improved ensemble learning approach for the prediction of heart disease risk
Heart disease is the leading cause of death globally, and early detection is crucial in
preventing the progression of the disease. In this paper, an improved machine learning …
preventing the progression of the disease. In this paper, an improved machine learning …
Medical Internet of things using machine learning algorithms for lung cancer detection
This paper empirically evaluates the several machine learning algorithms adaptable for lung
cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for …
cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for …
An application of transfer learning and ensemble learning techniques for cervical histopathology image classification
In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL)
and Ensemble Learning (EL) techniques in cervical histopathology image analysis …
and Ensemble Learning (EL) techniques in cervical histopathology image analysis …
Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images
Background CT findings of lung cancer and tuberculosis are sometimes similar, potentially
leading to misdiagnosis. This study aims to combine deep learning and content-based …
leading to misdiagnosis. This study aims to combine deep learning and content-based …
[HTML][HTML] Prediction of cancer disease using machine learning approach
FJ Shaikh, DS Rao - Materials Today: Proceedings, 2022 - Elsevier
Cancer has identified a diverse condition of several various subtypes. The timely screening
and course of treatment of a cancer form is now a requirement in early cancer research …
and course of treatment of a cancer form is now a requirement in early cancer research …
A diagnostic classification of lung nodules using multiple-scale residual network
H Wang, H Zhu, L Ding, K Yang - Scientific Reports, 2023 - nature.com
Computed tomography (CT) scans have been shown to be an effective way of improving
diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from …
diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from …