A Review on Medical Image Analysis Using Deep Learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072
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A Review on Medical Image Analysis Using Deep Learning
Dr. Rohini A. Bhusnurmath1, Sanasultana Alagur2
1Assistant Professor, Karnataka State Akkamahadevi Women’s university, Vijayapura, Karnataka, India
2Research Scholar, Karnataka State Akkamahadevi Women’s university, Vijayapura, Karnataka, India
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Abstract - Medical image analysis is the process of analyzing
medical images using computers with the goal of finding,
categorizing, and measuring patterns in clinical images.
Medical image processing and analysis requires the use of a
number of techniques, including segmentation, classification,
detection, localization, and registration. Medical image
analysis has made extensive use of deep learning, a quickly
expanding area of artificial intelligence. Deep learning
networks have shown successful in tasks including image
identification, segmentation, and classification. Examples of
these networks are convolutionalneuralnetworks(CNNs)and
recurrent neural networks(RNNs). Deeplearningisbeing used
in medical image analysis to explore and analyze large
amounts of data, which will increase the precision of
recognizing and diagnosing medical diseases. All things
considered, medical image analysis is essential to helping
medical practitioners diagnose patients accurately, plan
treatments, and keep track of their progress. The following
areas of application research are briefly summarized:
computerized pathology, neural, brain, retinal, pneumonic,
bosom, heart, breast, bone, stomach, and musculoskeletal.
Key Words: Deep Learning, Convolutional neural network,
Medical image, Brain tumor.
1. INTRODUCTION
Artificial intelligence (AI) models and deep learning
applications have the ability to improve people's lives in a
short period of time. Computer vision, pattern recognition,
image mining, and machine learninghaveall beenintegrated
into medical image processing, which includes picture
production, retrieval, analysis, and visualization. Deep
learning has created new opportunities for medical image
analysis because of its capacity to use neural networks to
discover patterns in data formats. Applications of deep
learning in healthcare cover a broad spectrum of problems,
such as infection control, individualized therapy
recommendations, and cancer detection. Modalities
including PET, X-ray, CT, fMRI, DTI, and MRI are frequently
used in medical images. In order to increase accuracy, deep
learning networks like convolutional neural networks
(CNNs) are frequently employed in medical picture
processing [1].
Medical imaging helps in disease research and identification
by taking pictures of internal organs for therapeutic use.
Clinical research and therapy effectiveness are the main
objectives of medical image analysis. Deep learning has
revolutionized this sector by showing hidden patterns for
flawless diagnosis and performing tasks like segmentation,
registration, and classificationwell.Numerousdeeplearning
techniques, such as convolutional neural networks and
pretrained models, are explored to enhance medical image
processing performance. These techniques are particularly
useful in organ segmentation, cancer detection, and illness
classification [2].
1.1 Medical Image Analysis Using Deep Learning
The main goal of medical image analysis is to identify the
anatomy's diseased regions so that doctors can better
understand how lesions progress. Four primary phases are
involved in the analysisofamedicalimage:(1)preprocessing
the image; (2) segmentation; (3) feature extraction; and (4)
pattern identification or classification. Preprocessing is the
process of enhancing image information for subsequent
processing or removing undesired distortions from
photographs.Thetechniqueofseparatingareasforadditional
research—such as tumors and organs—is referred to as
segmentation. Feature extraction is the process of carefully
selecting information from the regions of interest (ROIs) to
help identify them. Classification helps to categorize the ROI
based on features that are extracted [2][3].
Fig1: Steps of Medical Image Analysis

1.2 Deep Learning
Deep learning is a branch of machinelearningthatfocuses
on creating deep neural networks that are modeledafterthe
biological neural networks seen in the human brain. Recent
developments in deep learning have revolutionized the
interpretation of medical images, showing impressive
success in terms of precision, effectiveness, stability, and
scalability. By using advanced deep models, these
technologies can interpret intricate patterns found in
medical imaging, opening the doortomoreadvancedclinical
uses. Deep learning'shierarchical structuremakesitpossible
to extract minute features from medical images, greatly
advancing both clinical and scientific endeavors [3].
Deep learning-powered medical image analysis is
revolutionizing the field of medicine. Medical image
interpretation has been completely transformed by deep
learning techniques like convolutional neural networks
(CNNs) and recurrent neural networks (RNNs). These
cutting-edge methods provide unmatched accuracy and
efficiency in applications such as picture registration and
anatomical and cellular research. Deep learning algorithms
have recently achieved tremendous success in terms of
accuracy, stability, and scalability, pushing medical image
analysis to new heights [4].
Fig 2: Deep Learning Architecture
1.3 Convolutional Neural Network
Deep convolutional neuralnetworks(CNNs)havebecome
a game-changer in the field of illness diagnosis when applied
to medical pictureclassification. CNNsperformexceptionally
well on tasks like picture categorization, segmentation, and
illness detection by utilizing the powerofdeeplearning.With
itsconsiderablepotentialtoimprovediagnosticprecisionand
efficiency in medical imaging, this technology offers a bright
future for better healthcare results [5]. CNNs are specialized
artificial neural networks designed for computer vision,
inspired by the visual cortex of the human brain. CNNs
consist of convolutional, pooling, and fully connected layers
and are good at extracting features. Filters are used by
convolutional layers to extract features, pooling layers to
minimize spatial dimensions, and fully connected layers to
completeclassifications. CNNsarefrequentlyusedinmedical
image analysis and are excellent in identifying, categorizing,
and segmenting a wide range of medical diseases [6].
With a convolutional filter intended for 2D to 3D conversion,
this model exhibits the best fit for handling two-dimensional
input. It is very strongbecauseofitsremarkableperformance
strength and quick learning speed. Nonetheless, for the
classification process to work well, a significant amount of
labeled data must be provided. Convolutional Neural Nets
(CNNs) face several notableobstacles,suchasthepresenceof
local minima, a comparatively slower rate of convergence,
and a high degree of human factor interference. After
AlexNet's astounding success in 2012, CNNs have become a
crucial component in enhancing the efficiency of human
physicians in the field of medical image processing [1][6].
1.4 Recurrent Neural Network
Particularly useful for applications involving time series
or natural language processing, Recurrent Neural Networks
(RNNs) are a specific kind of artificial neural network that is
intended to analyze sequential input. RNNs provide a
memory mechanism that enables them to capture
dependencies within successive observations, which sets
them apart from typical neural networks. When generating
predictions or analyzing the current input, RNNs can take
into account the context of earlier inputs because to this
memory function. In applications like language modeling,
time series analysis, and speech recognition, where the
context and order of the data are critical, they perform
exceptionally well [1] [3] [4].
1.5 Long short-term memory/gated recurrentunit
networks (LSTM/GRU)
Recurrent neural networks (RNNs) with advanced
features, such as Long Short-Term Memory (LSTM) and
Gated Recurrent Unit (GRU), are employed in deep learning.
In order to overcome the vanishing gradient issue that
conventional RNNs have, LSTM andGRUarebothmadetobe
able to recognize long-term relationships in sequential data.
The network can store and retrieve data for longerthanksto
the introduction of memory cells, gates, and updating and
forgetting procedures by LSTM. A less complex version
called GRU is computationally more economical because it
can accomplish comparable results with fewer parameters.
Natural language processing, time-seriesanalysis,andmany
more fieldswherecomprehendingandmaintainingtemporal
connections are essential find use for these networks[1][3].
1.6 Autoencoder
Deep learning algorithms that are intended for
unsupervised learning are known as Autoencoder. The goal
of these neural networks is to compress the input data and
then use the compressed representation to recreate the
original data. Autoencoders are a versatile tool that may be
applied to a wide range of tasks, includingdata compression,

denoising, and feature learning. They are effective in
extracting meaningful representations from complex data
[3]. One deep learning model that best illustrates the idea of
unsupervised representation learning is the Autoencoder
(AE), as seen in Fig. 3a. When there are moreunlabelleddata
in the input data than labeled data, AE is helpful. In a lower-
dimensional space, z, AE encodes the input, x. Through a
single hidden layer, z, the encoded representation is once
more decoded to an approximate representation x′ of the
input x [4].
Fig 3: a Autoencoder [4] b Restricted Boltzmann Machine
with m visible and n hidden units [4] c Deep Belief
Networks [4]
1.7 Restricted Boltzmann Machine
An artificial neural network that is intended for
unsupervised learning is called a Restricted Boltzmann
Machine (RBM). RBMs are generative stochastic algorithms
that may be trained to learn a probability distributionovera
collection of inputs. Geoffrey Hinton invented them in 1985.
RBMs have random connections between their visible and
buried layers. Their applicationsinmachinelearninginclude
collaborative filtering, feature learning, dimensionality
reduction, and other areas. Natural language processing,
picture recognition, and recommendation systems arejusta
few of the fields in which RBMs are used [1][4].
2. DEEP LEARNING TECHNIQUE OF MEDICAL
IMAGING
An Overview of current techniques of DL for medical
imaging followed by various specifications considered for
selecting the classifiers and the analysis metrics used to
evaluate classification models.Theexistingliterature review
is divided according to the disease such as brain tumor, and
chronic kidney disease etc.
2.1 Brain Tumor
An abnormal growth of brain tissue is called a brain
tumor. It may be malignant (cancerous) or benign (non-
cancerous). The tumor could be primary (originating in the
brain) or secondary (spreading from other regions of the
body to the brain). Depending on where and how big they
are, brain tumors can impair a variety of bodily processes.
Symptoms include headaches, seizures, vision alterations,
and cognitive problems [7].
Nowadays, brain tumors are a serious and concerning
condition that impacts a lot of people. The primary cause of
brain tumors is the aberrant functioning of brain cells.
Primary and secondary brain tumors can be distinguished
from one another. While tumors in thesecondarystagegrow
larger and are referred to as malignant, early stage tumors
are small and deemed benign. According to the National
Brain Tumor Society, there are over 700,000 brain tumor
patients in the United States, ofwhich30.2%haveaggressive
brain tumors and 69.8% have benignones.Itisreported that
just 36% of people with brain tumors survive. Roughly
87,000 people received a brain tumor diagnosis in 2020 [8].
Fig 4: MRI brain images samples for two classes tumor
and no tumor [25].
2.2 Chronic Kidney Disease
Chronic kidney disease (CKD) is primarily caused by
diabetes and high blood pressure. Reduced renal function
over time is a result of chronic kidney disease
(CKD).Premature death is linked to chronic kidney disease
(CKD). Preventing the advancement of chronic kidney
disease (CKD) requires early diagnosis andidentification.To
diagnose CKD, researchers examine markers of kidney
disease including the Glomerular Filtration Rate (GFR). In
comparison to other modern machine learning methods, a
deep learning model has been built for the early
identification and prediction of CKD. To determine which
traits were most crucial for CKD identification, Recursive
Feature Elimination (RFE) was employed [24].

Fig 5: Images for Healthy Kidney and Diseased Kidneys
[31]
3. LITERATURE SURVEY
This section covers survey studies on deep learning-
based methods for medical image analysis, Brain tumor,
and chronic kidney disease.
Muhammad Imran Sharif et al. [8] focus on high
accuracy, research aims to improve multiclass brain tumor
classification in medical imaging. We introduce an
automated deep learning method based on the
Densenet201 model, optimized by deep transfer learning
on data that is not balanced. Extraction of features fromthe
average pool layer obtains comprehensive tumor data. We
present feature selection methods(EKbHFVandMGA)with
additional refinement via a unique threshold function to
overcome accuracy constraints. Using a multiclass SVM
cubic classifier, our method uses a non-redundant serial-
based strategy to merge these characteristics. Without
augmentation, experimental validation on the BRATS2018
and BRATS2019 datasets achieves over 95% accuracy. A
comparative study using alternative neural networks
demonstrates the importance and performance of our
suggested approach in improving the categorization of
brain tumors.
Aniwat Phaphuangwittayakul, et al. [9] Conducted a
systematic review of Showcasing advances in medical
imaging and diagnostic applications, an ideal deeplearning
framework specifically designed for traumatic brain injury
has been established for the detection and quantificationof
multi-type hemorrhagic lesions in head CT images.
Gunasekaran Manogaran, et al. [10] Introduced an
enhanced machine-learning method based on orthogonal
gamma distribution is presented in this study to analyze
regions affected by brain tumors and identify anomalies
using automatic ROI identification. Employing a machine
learning technique to measure the sensitivity and
selectivity parameters,theresearchtacklestheissueofdata
imbalance in the abnormality zone by sampling the edge
coordinates. By employing a mathematical formulation,the
study verifies the algorithm's mean error rate, efficiency,
accuracy, and optimal automatic identification for both
tumor and non-tumor areas. With reference to MRI
applications especially, the research advances the field of
brain abnormalities identification and analysis in the
healthcare industry.
Carlo Tappero, et al. [11] Worked on the study
investigates whether post-mortem CT (PMCT) can be used
to detect cerebral hemorrhages in bodies that have
decomposed. With post-mortem decomposition, the study
seeks to show that PMCT is still able to detect brain
hemorrhages. The implications of the detrimental effects of
decomposition on the consistency of brain tissue makethis
especially important. The study unveils the potential of
PMCT imaging in forensic pathology by examining and
validating its capacity to locate and diagnose cerebral
hemorrhages in deteriorated remains.
JAEHAK YU, et al. [12] performed a machine learning-
based system for predicting the risk of stroke using PPG
and ECG biomarkers. Using machine learning approaches,
the proposed system seeks to predict and semantically
understand stroke prognostic signs. In order to construct
this system, a variety of bio-signal data are recorded and
gathered, such as electroencephalography (EEG),
electrocardiography (ECG), and electromyography (EMG).
Additionally, for the AI-based stroke disease prediction
module, a multimodal bio-signals method is investigated.
Rohit Lamba, et al.[13]Concentratedonearlydetection,
the scientists suggest a hybrid Parkinson's disease
diagnosis method based on speech signals. To create the
model with the best performance, they experiment with
different feature selection strategies and classification
algorithms. There are three feature selection techniques
used: genetic algorithm, extra tree, and mutual information
gain. There are three classifiers used: random forest, k-
nearest-neighbors, and naive bayes. The best result is
obtained with 95.58% accuracy when the evolutionary
algorithm and random forest classifier are combined,
outperforming earlier research in the literature.
S. Deepak, et al. [14] Focus on automating the
classification of brain cancers from MRI pictures by

combining Support Vector Machine (SVM) and
Convolutional Neural Network (CNN) characteristics. The
CNN was created expressly to extract pertinent features
from brain MRI pictures. After that, a multiclass SVM is
combined with these collected features to improve brain
tumor classification performance. An open-source MRI
dataset from Figshare that depicts three different kinds of
brain tumors is used to assess the suggested method. A
completely automated brain tumor classification system
with increased accuracy is the goal of integrating CNN and
SVM.
Sukhpal Kaur, et al. [15] conducted an improving
Parkinson's disease diagnosis by combining transfer
learning, data augmentation, and a deep Convolutional
Neural Network (CNN). The study attempts to increase
diagnostic accuracy by utilizing the power of a deep 25-
layer CNN classifier (AlexNet). Transferlearning makesuse
of previously learned information for the model, and data
augmentation makes the model more robust by artificially
growing its dataset. The objective of the suggested
methodology is to offer a sophisticated and precise
Parkinson's disease diagnosis tool.
Sidra Sajid, et al. [16] proposed on deep learning
method for segmenting and identifying brain tumorsinMR
images is presented in this work. The method merges
contextual and local data using hybrid CNN architecture
with a patch-based strategy. Dropout regularization and
batch normalization are used to reduce over fitting. A two-
phase training approach is applied to address the
imbalance in data. A CNN-based feed-forward pass comes
after preprocessing for image normalization and bias field
correction.
Loveleen Gaur, et al. [17] focuses on workable way to
identify COVID-19 from chest X-rays and differentiate
between healthy individuals and those suffering from viral
pneumonia. The research uses deep learning methods,
more especially deep convolutional neural networks
(CNNs), to examine medical images, mostly X-rays of the
chest. The aim is to improve COVID-19 detection accuracy
while tackling issues like differentiating COVID-19 from
other respiratory disorders. The suggested method addsto
the current efforts to use cutting-edge technology forquick
and precise COVID-19 diagnosis using medical imaging.
Table -1: Comprehensive Analysis
4. METHODOLOGY
Our target topic is Medical image analysis using deep
learning. We ended up using around 30 of the most recent
papers related to medical image analysis using deep
learning. Some of the papers examined only deep learning,
while other used a combination of Machine Learning and
deep learning.
To find the papers for our search, we mostly used the
Scopus database. This is to keep non-refereed publications
out of it. On the other hand, we show the distribution of a
few chosen papers among the current databases in Figure 4.
PubMed, ScienceDirect (Elsevier),IEEE,andSpringerarethe
top four databases
Distribution of Papers
Across Databases
Springer
IEEE explore
ScienceDirect
PubMed
Other
Fig 6: Distribution of Papers across Databases

It is evident that the use of deep learning for medical
image analysis and brain tumor research articles peaked
between 2019 and 2023. We focused solely on journal and
conference articles, reducing the total number of papers to
30, and we only included studies that used genetic
expression and imaging. In addition to several forms ofgene
expression and gene sequencing,theimagingmodalitiesthat
we took into consideration included ultrasound,
radiography, mammography, and magnetic resonance
imaging (MRI). Our research focuses on articles that employ
deep learning to implement medical image analysis and
studies that forecast brain tumors using both gene and
image data. For every article, we used the following
eligibility standards: (1) The work is written in English; (2)
It addresses the diagnosis and treatment of brain tumors;
and (3) It talks about machine learning and deep learning
hybrid models. (4)The study discusses deep learning only;
(5) genetic expression data;(6)imagingdata;(7)journal and
conference publications are the only ones kept; (8)
Convolutional and recurrent neural networksarecovered in
the paper; (9) The paper focuses on deep learning-based
medical picture analysis; (10) Only papers pertaining to
medicine or biomedical engineering are retained.
Please be aware that the study did not include any non-
refereed papers. First, we noted the essential details,likethe
title of the work, the year it was published, the listofwriters,
and the publisher. Then, we incorporated certain data to
carry out the systematic review, like the dataset, the
features, the recorded accuracy and other performance
assessment metrics, the algorithm employed, and if the
publication talks simply deep learning or a hybrid between
DL and ML, among many other columns. These standards
helped us respond to our study inquiries.
5. CONCLUSION
This article provided a review of the most recent
research on deep learning for medical imaging.Ittalksabout
a noteworthy contribution in the following fields. Firstly, a
detailed review of the core ideas of Deep Learning is
discussed. Consider this section of the review as a lesson on
common medical imaging Deep Learning principles.Second,
a thorough summary of Deep Learning-based methods in
Medical Imaging was given by the study. Later in the paper,
the main problems that Deep Learning encounters when
analyzing medical images arediscussed,alongwithpotential
solutions. This work assessed the progress made by CNN-
based deep learning algorithms in clinical applications like
object detection, segmentation, registration, and image
classification. Several technical issues were covered in the
research, including data problems, machine and hospital
integration, robust systems, data preprocessing, ongoing
model learning, and cross-system fine tuning. Accordingtoa
review of the literature, the DNN classifier outperforms
traditional classifiers in terms of accuracy. AI-based image
evaluation can identify complicated imaging patterns that
are impossible to detect using visual radiologic evaluation.
The study also showed that DL tools are beneficial to
radiologists and clinics. According to that study, humans
who use AI perform better than those who do not.
6. ACKNOWLEDGEMENT
I would like to thank my esteemed mentor and guide
from the bottom of my heart. Dr. Rohini A. Bhusnurmath,
Assistant Professor in the computer science department at
Karnataka State Akkamahadevi Women's University,
Vijayapura. For her technical guidance, support, and helpful
criticism that inspired me to pursue excellence even more.
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