Search Results

• Leveraging degraded SDCTs for screened lung nodule classification.
• Application of radiomics on lung nodules for developing a more explainable model.
• Potential of shape and size features to enhance lung nodule classification ...

Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these ...

In clinical practice, the diagnosis of malignant nodules mainly relies on the experience of doctors and pathological examination, with a high misdiagnosis rate and serious waste of resources. Therefore, there is an urgent need for a highly accurate and ...

Recently, lung cancer is observed as the most deadly disease throughout the world with a high mortality rate. The survival rate with lung cancer is minimal due to the difficulty in detection of cancer in early stages. Various screening techniques ...

Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. Using hybrid deep learning techniques to detect nodules will improve the sensitivity of lung ...

• A causal discovery algorithm based on rank correlation.
• Apply Kendall correlation coefficient to causal discovery.
• Lung nodule detection algorithm based on rank correlation.
• Results demonstrate the feasibility of the proposed ...

Early diagnosis can significantly improve the survival rate of lung cancer patients. This study attempts to construct a causal structure network between the computational and semantic features of lung nodules through causal discovery algorithms, ...

• An efficient 3-D computer-aided classification (CADx) model improved from the split-transform-merge convolution neural network (CNN) architecture is used for ...

Lung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection ...

Lung Segmentation is one of the pre-processing steps for lung cancer diagnosis. Segmentation of lung contour is challenging when the nodules are attached to the surrounding tissues of the lung, such as juxta-pleural boundary or vasculature. This ...

The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early ...

Lung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules ...

This work presents a novel method that uses a Vector of Pre-processing Filters combined with simple relational and Boolean equations for pulmonary nodule detection. To isolate nodules from other lung structures, we propose a 16 filter ...

• The results confirm the superior effectiveness of the proposed method.
• Highest ...

Lung cancer is the most frequent cause of cancer mortality in the world. The diagnostic procedure usually begins with a chest X-ray; however, it is difficult to interpret due to the set of anatomical structures overlapped. Computer-aided detection ...

Lung cancer is one of the deadliest cancers all over the world. One of the indications of lung cancers is the presence of the lung nodules which can appear individually or attached to the lung walls. The early detection of these nodules is crucial ...

Lung cancer is of the most serious and common type of cancers. It is usually diagnosed in the final stages which make it hard to treat. Now days, many techniques are to help in the detection of lung cancer, but still there is further need to ...

We present the LUng CAncer Screening (LUCAS) Dataset for evaluating lung cancer diagnosis with both imaging and clinical biomarkers in a realistic screening setting. We extract key information from anonymized clinical records and radiology reports,...

Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance – positive class (lesion) size compared to negative class (non-lesion) size; lesion size ...

An intelligent clinical decision support system is proposed classifying lung nodules for lung cancer prediction using a kernel-induced random forest classifier. A contourlet filter is used for image denoising. Fuzzy logic is used to represent the ...

Lung cancer is one of the leading causes of death worldwide. Its early detection in its nodular form is extremely effective in improving patient survival rate. Deep learning (DL) and especially Convolutional Neural Network (CNN) have an important ...

Lung cancer has always threatened people's health and life. Lung nodules, as early features of lung cancer, have very important clinical significance and research value for the diagnosis of lung cancer. The features captured by the traditional ...

Feature extraction of lung nodules is very important in the diagnosis of lung cancer and is the premise of feature description, target matching, recognition and benign and malignant diagnosis. The main contribution of this work is the development of a ...

Early detection of pulmonary lung nodules plays a significant role in the diagnosis of lung cancer. Computed tomography (CT) and chest radiographs (CRs) are currently being used by radiologists to detect such nodules. In this paper, we present a novel ...