Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

Because of the tumor with infiltrative growth, the glioma boundary is usually fused with the
brain tissue, which leads to the failure of accurately segmenting the brain tumor structure …

Lung cancer is the second most prominent cancer in men and women, and it is also the
leading cause of cancer-related mortality. If lung cancer is diagnosed early, when it is …

Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic
imaging for various diseases and modalities and therefore has a high potential to be used …

Visual classification for medical images has been dominated by convolutional neural
networks (CNNs) for years. Though they have shown great performance on accuracy, some …

We propose a two-stage Convolutional Neural Network (CNN) based classification
framework for detecting COVID-19 and Community Acquired Pneumonia (CAP) using the …

In view of the low diagnostic accuracy of the current classification methods of benign and
malignant pulmonary nodules, this paper proposes a 3D segmentation attention network …

Numerous advancements in various fields, including pattern recognition and image
classification, have been made thanks to modern computer vision and machine learning …

Many medical applications need to be able to separate and find brain tumor's using CT scan
images. There have been a lot of recent studies that used distinguish between benign and …