Utilizing Deep Learning Techniques for Lung Cancer Detection

Title

Utilizing Deep Learning Techniques for Lung Cancer Detection

Creator

Madasu S.; Rolla K.J.; Patil S.C.; Gupta R.; Logeshwaran J.

Description

Deep learning can extract meaningful insights from complex biomedical statistics, which includes Radiographs and virtual tomosynthesis. Traits in contemporary deep studying architectures have enabled faster and more correct mastering of the functions gifted in clinical imagery, main to better accuracy and precision in medical analysis and imaging. Deep studying strategies may be used to pick out patterns within the pics which may be indicative of illnesses like lung cancer. Those ailment patterns, which include small lung nodules, can be used for early detection and prognosis of the sickness. Recent studies have employed deep learning strategies consisting of Convolutional Neural Networks (CNNs) and switch learning to come across most lung cancers in CT pictures. The first step in this manner is to generate datasets of pictures of the lungs, each from wholesome people and those with most lung cancers. Those datasets can then be used to teach a deep knowledge of a set of rules that may be optimized to it should locate those styles. Once educated, the version can be used to come across styles indicative of lung most cancers from new take a look at images with high accuracy. For further accuracy and reliability, extra up-processing techniques, along with segmentation and records augmentation, may be used. Segmentation can be used to detect a couple of lung nodules in a photo, and records augmentation can be used to lessen fake high quality outcomes. 2024 IEEE.

Source

2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Architectures; Augmentation; Convolutional; Radiographs; Segmentation

Coverage

Madasu S., Microsoft, Charlotte, NC, United States; Rolla K.J., Independent Researcher, Fort Mill, SC, United States; Patil S.C., United States; Gupta R., Tejankar Hospital, MP, Ujjain, India; Logeshwaran J., CHRIST (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISBN: 979-835037024-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCCNT61001.2024.10724114

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213323501&doi=10.1109%2fICCCNT61001.2024.10724114&partnerID=40&md5=0eb609ff194a8a06f3bb18910cfa7bcb