A novel framework for Detecting Breast Cancer, Brain Tumor & Pneumonia Using Deep Learning Techniques

Brain tumours, breast cancer, and pneumonia are significant diseases causing a substantial number of deaths globally. These diseases affect a great part of the world, including India. Research on early detection strategies is critical for saving lives. This proposal describes a unique way to use deep learning approaches to detect three common diseases: breast cancer, brain tumours, and pneumonia. This approach aims to improve treatment outcomes and diagnostic accuracy by integrating a range of medical imaging modalities tailored to individual diseases. In similar challenges, DCNNs have demonstrated remarkable speed and accuracy, demonstrating their potential to enhance medical diagnosis. DCNN models can help medical professionals by providing accurate and automated illness identification, especially for areas with a shortage of radiologists.

Authors

Publication Details

Journal
Journal of Informatics Education and Research
Published
2024-12-06
DOI
https://doi.org/10.52783/jier.v4i3.1812
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel framework for Detecting Breast Cancer, Brain Tumor & Pneumonia Using Deep Learning Techniques

K. Sagar
Journal of Informatics Education and Research
AI in cancer detection
article

A novel framework for Detecting Breast Cancer, Brain Tumor & Pneumonia Using Deep Learning Techniques

K. Sagar
article en

Abstract

Brain tumours, breast cancer, and pneumonia are significant diseases causing a substantial number of deaths globally. These diseases affect a great part of the world, including India. Research on early detection strategies is critical for saving lives. This proposal describes a unique way to use deep learning approaches to detect three common diseases: breast cancer, brain tumours, and pneumonia. This approach aims to improve treatment outcomes and diagnostic accuracy by integrating a range of medical imaging modalities tailored to individual diseases. In similar challenges, DCNNs have demonstrated remarkable speed and accuracy, demonstrating their potential to enhance medical diagnosis. DCNN models can help medical professionals by providing accurate and automated illness identification, especially for areas with a shortage of radiologists.

Journal of Informatics Education and ResearchVol. 4(3)
Good health and well-being
Openalex Percentile: Top 80%
AI in cancer detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.