The large number of images and the subtle characteristics of pulmonary nodules make it challenging to detect the lung cancer from CT scans. Complex and small nodules may provide a less accurate diagnosis, and manual examination is time-consuming and result to variation among observers. Previous studies have shown excellent accuracy but often fall short in terms of organization and understandability. This paper introduces an automated and explainable deep learning framework for lung nodule classification. CNN is used to classify CT scans. We use consistent preprocessing methods to make sure that model is strong and delivers reliable results. Experiments conducted on public lung CT datasets show an accuracy of about 95%.
Abstract: Deep learning has great potential for environmental monitoring, yet real-world applications often face challenges from large-scale, multimodal, and noisy datasets. We introduce GreenNet, a flexible and open-source framework that makes it easier to build and scale deep l…
Surveillance anomaly detection systems built around a single monolithic deep network are difficult to interpret, brittle to distribution shift, and offer operators no rationale on which to act. This paper presents VisionGuard, an explainable deep learning framework that reorganiz…
Large language models (LLMs) are increasingly proposed as reasoning engines for cyber threat intelligence (CTI) triage and threat hunting, promising to reduce the alert-fatigue burden long documented in security operations research. However, most reported evaluations emphasize ra…
Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, bronchiectasis, bronchiolitis and upper respiratory tract infection (URTI) remain among the leading causes of illness and death worldwide. Conventional diagnosis relies heavily on auscul…
Abstract: Deepfake technology, driven by generative models such as GANs and diffusion architectures, has enabled the creation of highly realistic manipulated media capable of deceiving both visual and auditory perception. Such forgeries pose significant risks to identity verifica…
Dermatological disorders remain a significant global healthcare challenge, affecting millions of individuals and contributing to increased disease burden, particularly when delayed or inaccurate diagnosis affects treatment outcomes. Although artificial intelligence has demonstrat…