Critical Analysis: Deep Learning in Medical Image Classification

This report critically analyzes the application of deep learning, particularly convolutional neural networks (CNNs), in the classification of medical images. It begins with an introduction highlighting the potential of machine learning algorithms in the medical field, especially in image analysis. The report explores the conceptual terms of deep learning, its impact on medical image classification, and recommends techniques for feature extraction. A literature review examines existing research, discussing how deep learning is used to identify and classify medical images, including blood cells. The methodology section outlines the research approach, including qualitative methods like literature reviews, an inductive research approach, and an interpretivism philosophy. Data collection methods, including primary research through questionnaires, and data analysis using thematic analysis are described. Ethical considerations, such as participant confidentiality and avoiding harm, are also addressed. The report concludes by emphasizing the importance of deep learning in improving medical diagnostics and treatment through enhanced image analysis.

Deep learning in classification of medical
images
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Contents
INTRODUCTION...........................................................................................................................3
LITERACTURE REVIEW.............................................................................................................4
RESEARCH METHODOLOGY....................................................................................................5
REFERENCES................................................................................................................................7
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Aim: To critically analyse the classification of medical image by using deep learning and
convolutional neural networks.
INTRODUCTION
Machine learning algorithm that have potential to be examined deeply in medical fields.
From the success of altering the way in which medicine is practiced. Furthermore, medical image
analysis is consider as an active field in term of research study.
The background of deep learning technique when adopting in 20th century when collected
the data or information is relatively labelled, structured which likely that this will be different
areas. But Nowadays, it will be using in term of artificial intelligence which capable of learning
unsupervised from data. In context of medical field. It will help for classification of image cells,
learning the deep neural network.
The report will discuss about the concept of deep learning technique that mainly used in
the medical image classification. Furthermore, it will determine the alternative technique, which
help for recognising the feature extraction process in term of medical image divisions.
Problem statement
The importance of research study is to analyse about the machine learning, which have
become potential to be invested deeply in the medical field. Researcher will gain the more
information or data which help for clinical decision-making. Furthermore, scholar will be
gathering the detailed about the classification of image. In order to generate the accurate result or
outcome. Through this research study, it supports for providing the better treatment by using
deep learning technique and also resolve the issue or problem. In this research project, it will be
explaining the research problem about the conceptual term of deep learning method that impact
on the impact of the classification within medical image
Objective
 To understand the conceptual term of deep learning method.
 To identify the impact of deep learning into classification of medical image.
 To recommend deep learning technique in feature extraction process of medical image
analysis.
Research question
 What are the conceptual term of deep learning method?
 What are impact of deep learning into classification of medical image?
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 What are recommend deep learning technique in feature extraction process of medical
image analysis?
LITERACTURE REVIEW
Theme: 1 conceptual term of deep learning method and impact g into classification of medical
image
According to Karimi, Dou and Gholipour (2020) deep learning is based on the approach
that mainly involved critical analysis, facts and figures. Sometimes, it is linking with medical
field for purposed to identifying image. Another way, it is also known as machine learning
technique that can easily analyse, apply, evaluate and create the factual knowledge. It provide the
facility to classify the image of blood cell by using deep learning technique.
Kiani and Martin (2020) said that deep learning has achieved the accuracy level in
context of different classification. In this way, it help for predicting task in the form of images,
speech. However, Deep learning technology is largely at the different level which providing the
factual knowledge in the form of features. Sometimes, it is extending features representation
learning.
According to Möckl, Roy and Moerner (2020) Deep learning is an appropriate for
classification of medical image because it provide the accurate division of cell image. It help for
medical professional to identify the level of disease. Generally, this method is used for purpose
to determine the certain human condition in context of health illness. So as it support for
identifying the accurate image. It also easier for medial professional to give further better
treatment.
Samaniego and et.al. (2020) said that deep learning technique allows to divided into
different layers which allows complex non-linear relationship. Furthermore, it is applying the
machine learning for extraction and representation lies at different level. Conventionally, task
related features that were designed by medical professional. In order to gain more knowledge
about the target domain. Sometimes, it is very challenging to gather information about the image
classification. However, deep learning that has relieved such obstacle by determining features
step into learning phase. The positive way to impact on the image classification and also
generating the accurate details. It is best way to recognize the information or data while
interpreting in medical term. Many professional use this deep learning technique to recognize the
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health issue among people. Afterwards, categorizing the problem within different image format
so as it easier for finding the accurate solution in proper manner.
RESEARCH METHODOLOGY
It refer to the sequential process or method, which is mainly used for gathering large amount
of information or data related the research project. it is the best way in which implement the
different approaches, philosophy to handle the different activities.
Research method that mainly used for collecting large amount of data related the deep
learning in context of medical image analysis. It can be divided into different group: qualitative
and quantitative method. In qualitative method, The scholar will Primary research method by
Literature review, for collecting the data related to the research topic. This method is basically
used pre-existing theories so that it can easily understand about the previous study. In order to
find out the current strength, weakness of research methodology.
Research approach refer to the procedure that consider the assumption, belief and
thoughts about the research project. The research approach can be categorised into different
types: deductive, inductive and Abductive. The scholar will choose inductive research approach
and further start with the observation, theories which are proposed in the research processes. It
also useful for researcher to identify the different pattern from the explanation, observation.
Research philosophy is based on the practical implication and perform the hypotheses on
the basis of data or information. Generally, it is helping for deal with the knowledge
development, nature and multiple sources. There are different way to divide the philosophy such
as positivism, intepretivism, realism and pragmatism The scholar will choose the intepretivism
Philosophy that mainly include various interpret elements. Thus, it also investigate human
interest within the research study. Sometimes, researcher may assume the reality while accessing
the data in proper manner.
Research design is based on the technique that help for researcher to interpret the data
into significantly. Another way, it may refer to specific choice of research when they can easily
define the overall design pattern of research project. It also categorised into different types:
exploratory and conclusive. The researcher will use the exploratory research design which help
for exploring research questions. It does not intend to provide the accurate solution. It help for
identifying the research problem and considering an appropriate conclusion.
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Data collection is based on the process of collecting a large amount of data related the
research project. Data collection method can be divided into different types: primary and
secondary. The scholar will select the primary method, which help for acquiring a lot of
information. Researcher use primary method to collect relevant data by using Questionnaires.
Data analysis is a process for interpreting the suitable elements which are relevant to the
research project. Researcher will be doing data by using thematic analysis. In context of
qualitative method, it is considered the best way in which identify the relevant information.
Sampling is defined as specific principle that mainly used for selecting the member
within large number of population. Researcher will select simple random technique to assign
specific task of every participant in research processes. The sample size is 30 medical
professional.
Ethical consideration
 Research participant member should not be subjected, harm of other members.
 It always maintain the dignity of participant members.
 Maintain the confidentiality level in context of research data or information.
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REFERENCES
Book and Journals
Karimi, D., Dou, H. and Gholipour, A., 2020. Deep learning with noisy labels: exploring
techniques and remedies in medical image analysis. Medical Image Analysis. p.101759.
Kiani, A. and Martin, B.A., 2020. Impact of a deep learning assistant on the histopathologic
classification of liver cancer. NPJ digital medicine. 3(1). pp.1-8.
Möckl, L., Roy, A.R. and Moerner, W.E., 2020. Deep learning in single-molecule microscopy:
fundamentals, caveats, and recent developments. Biomedical Optics Express. 11(3).
pp.1633-1661.
Samaniego, E. and et.al., 2020. An energy approach to the solution of partial differential
equations in computational mechanics via machine learning: Concepts, implementation
and applications. Computer Methods in Applied Mechanics and Engineering. 362.
p.112790.
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