Chest X-ray image used for detection of lung cancer. The early detection and treatment of lung nodule are the most challenging clinical tasks performed by radiologists. In this paper, processing the X-ray images using various image processing algorithms like Genetic algorithm, K-nearest neighbour ...
we would detect on average 394 additional lung cancer cases, or an ~8-fold increase (95% CI, 4.4- to 19.6-fold increase) compared to LDCT alone (Fig.6d). The combined approach would not only substantially improve detection of lung cancer, but would be expected to increase the accuracy of...
In medical imaging, the last decade has witnessed a remarkable increase in the availability and diversity of chest X-ray (CXR) datasets. Concurrently, ther
In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure....
et al. Combined use of intraoperative ultrasound and indocyanine green fluorescence imaging to detect liver metastases from colorectal cancer. HPB 15, 928–934 (2013). Article PubMed PubMed Central Google Scholar Gotoh, K. et al. A novel image-guided surgery of hepatocellular carcinoma by indocy...
This paper proposes a multi-kernel-size spatial-channel attention method to detect COVID-19 from chest X-ray images. Our proposed method consists of three stages. The first stage is feature extraction. The second stage contains two parallel multi-kernel-size attention modules: multi-kernel-size ...
The proposed method for using Canny edge detection for X-ray lung medical image classification consists of several stages. The color image is read from a database that has several medical images collected from the web; then, these images are resized into smaller sizes in order to facilitate the...
We found that CNNs which are effective at natural image recognition tasks, can be implemented to distinguish between the most common histopathologic subtypes in NSCLC. With enough labeled examples, CNNs can detect subtle differences in images to predict phenotypes in future cases14. Using pre-traine...
To detect the Pneumonia cases from chest X-rays, an effective CNN algorithm is applied with the 121-layered convolutional neural network. The model is trained on a data set containing over 100,000 images providing frontal-view of lung X-ray and describes 14 types of ailments. Rajpurkar et ...
3.1.4. Chest X-ray image preparation The CXR images were cropped to the area of interest for all experiments. This step uses a pre-trained U-Net [45] model for lung segmentation, used in [21]. This step helps the neural network to focus on the lungs instead of considering the surround...