TumorDetNet: A unified deep learning model for brain tumor detection and classification
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment
process and helps to save the lives of a large number of people worldwide. Because they …
process and helps to save the lives of a large number of people worldwide. Because they …
An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model
Introduction Recently, plant disease detection and diagnosis procedures have become a
primary agricultural concern. Early detection of plant diseases enables farmers to take …
primary agricultural concern. Early detection of plant diseases enables farmers to take …
A lightweight deep learning-based model for tomato leaf disease classification
Tomato leaf diseases significantly impact crop production, necessitating early detection for
sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying …
sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying …
A Novel Deep Learning Approach for Colon and Lung Cancer Classification Using Histopathological Images
Colon and Lung cancers are two of the most common causes of mortality in adults. They
may simultaneously form in organs and have a detrimental effect on human life. There is a …
may simultaneously form in organs and have a detrimental effect on human life. There is a …
Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images
Early detection of brain tumors is vital for improving patient survival rates, yet the manual
analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This …
analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This …
Bridging Clinical Gaps: Multi-Dataset Integration for Reliable Multi-Class Lung Disease Classification with DeepCRINet and Occlusion Sensitivity
This research presents DeepCRINet, a deep learning (DL) model designed for reliable
performance across various Chest Radiography Images (CRIs) datasets, in response to the …
performance across various Chest Radiography Images (CRIs) datasets, in response to the …
Detection of multi‐class lung diseases based on customized neural network
In the medical image processing domain, deep learning methodologies have outstanding
performance for disease classification using digital images such as X‐rays, magnetic …
performance for disease classification using digital images such as X‐rays, magnetic …
A Deep Learning Approach for Intelligent Diagnosis of Lung Diseases
Abstract According to the World Health Organization (WHO), lung diseases contribute to
millions of fatalities globally each year. Pneumonia stands out as a leading cause, claiming …
millions of fatalities globally each year. Pneumonia stands out as a leading cause, claiming …
LUN-Net: Deep Learning Approach Based Medical Image Processing for Lung Cancer Detection
DSS Suggu, N Vedula, P Pandiyarajan… - … on Intelligent Cyber …, 2024 - ieeexplore.ieee.org
This research study proposes LUN-Net, a deep learning-based method for lung cancer
detection, which represents a substantial breakthrough in medical image processing. Using …
detection, which represents a substantial breakthrough in medical image processing. Using …
Utilized CNN Model for Lung Diseases Detection
Abstract According to the World Health Organization (WHO), the leading causes of mortality
in the globe are pneumonia, COVID-19, tuberculosis, and pneumothorax. Therefore, early …
in the globe are pneumonia, COVID-19, tuberculosis, and pneumothorax. Therefore, early …