Microscopic biopsy image reconstruction using inception block with denoising auto-encoder approach

The use of computer-aided image analysis for disease diagnosis and prognosis has dramatically increased during the past 10 years. The introduction of computer-assisted image analysis of images produced by equipment employed in medical science for the prognosis of various diseases has been made possi...

Full description

Saved in:
Bibliographic Details
Published in:International journal of information technology (Singapore. Online) Vol. 16; no. 4; pp. 2413 - 2423
Main Authors: Singh, Shiksha, Kumar, Rajesh
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.04.2024
Springer Nature B.V
Subjects:
Artificial Intelligence
Biopsy
Computer Imaging
Computer Science
Data acquisition
Datasets
Deep learning
Digital imaging
Feature maps
Frequency distribution
Histopathology
Image analysis
Image Processing and Computer Vision
Image quality
Image reconstruction
Image resolution
Literature reviews
Machine Learning
Magnetic resonance imaging
Mammography
Medical equipment
Medical imaging
Medical science
Methods
Noise
Noise reduction
Original Research
Pattern Recognition and Graphics
Prognosis
Radiation
Signal processing
Software Engineering
Ultrasonic imaging
Vision
X-rays
Histopathological image
Medical image
Denoising autoencoder
Inception network
Convolutional neural network
ISSN:2511-2104, 2511-2112
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The use of computer-aided image analysis for disease diagnosis and prognosis has dramatically increased during the past 10 years. The introduction of computer-assisted image analysis of images produced by equipment employed in medical science for the prognosis of various diseases has been made possible by the development of digital scanners and high-resolution imaging technologies. Additionally, much like any other digital image, the obtained medical images may become distorted by various distortions and sounds. The noise in histopathology images can be random or have an even frequency distribution; it can be introduced during data acquisition by imaging devices or as a result of signal processing algorithms; either way, the noise appears in the digital image in an uncorrelated way, inevitably lowering the visual quality of the images. In the present article, employed an integrative strategy for the image denoising of the histopathological images of Breast cancer through deep learning (DL) approach. Moreover, some noises remain intact while following the approach due to the direct conduction of the feature map and also the level noise. Henceforth, a denoising model using Denoising Autoencoder with an Inception network block (InDAENET) has been proposed to deal with the left-out feature maps and high noise signals related to the medical images. The proposed method has been prepared and verified on the publicly available dataset BreakHis. This approach has further been successfully compared with the conventional Denoising autoencoder (DAE) method. Henceforth, it paves the way for Histopathological Image Reconstruction using an Inception block with Denoising Autoencoder for Breast Cancer Detection.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01658-0