In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models.#Three low-dose C
3.1. Chest CT dataset In the implementation of our second model, we focused on the chest CT scan dataset retrieved from Kaggle [22] (https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images). This dataset, which contains diverse chest cancer types—large cell carcinoma (LCC), adeno...
From time to time, lung cancer has appeared in the category of nearly the most lethal maladies since humankind existed. It is even among the most incessant fatalities and major reasons of mortality among all cancers. Lung cancer cases are significantly g
machine-learning computer-vision deep-learning jupyter-notebook python3 medical-imaging image-classification chest-xray-images cnn-keras kaggle-dataset pneumonia-detection deep-ne lung-disease Updated Jul 30, 2020 Jupyter Notebook nivation / Chexnet Star 0 Code Issues Pull requests Image classif...
Kuan, K., et al.: Deep learning for lung cancer detection: tackling the kaggle data science bowl 2017 challenge. arXiv preprint arXiv:1705.09435 (2017) 2. Chon, A., Balachandra, N., Lu, P.: Deep convolutional neural networks for lung cancer detection. Standford University (2017) 3. ...
This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan...
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging. 2015;15:27. Article Google Scholar Haider MA, Vosough A, Khalvati F, Kiss A, Ganeshan B, Bjarnason GA. CT texture analysis: a potential tool for prediction...
[30]) is one of the largest publicly available datasets containing chest X-rays with not only labels, but with annotated pneumothorax location on the image. The dataset, which comprises 12047 chest X-ray images along with their respective annotated masks, was utilized in a Kaggle competition ...
Fig. 1. CT slices for 2D and 3D CNNs respectively. Issues like lack of high-quality data, image complexity, annotated data, and imbalanced datasets make it more difficult for neural networks to learn how to detect and diagnose lung cancer. Therefore, it is essential to create reliable DL ...
The use of the dataset for the segmentation model training process comes from the address https://www.kaggle.com/datasets/anasmohammedtahir/covidqu [41], which contains CXR images along with area masks. The amount of original data from the source is 33,920 image data X-rays. The following...