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Computer-aided Bio-medical Tools Disease Prediction using Machine Learning 53
https://t.me/med1917
Keywords: Biomedical image processing, CAD, Combined algorithm, Cancer
diagnosis, Covid-19, Classification, Feature extraction, Pre-processing,
Segmentation.
INTRODUCTION
Computer-based medical image analysis is a crucial task for physicians in
diagnosing new diseases. In biomedical research, computer-assisted detection
(CAD) has become widespread, even generating its own field of study. However,
it cannot replace a physician, it serves as an assisting device in making accurate
and quick decisions. It may prove an asset especially during medical emergencies.
CAD can be broadly categorized into CA detection and CA-Diagnosis. CA
detection involves marking visible parts in images, while CA-Diagnosis is used to
analyze the identified structures.
Deep Learning (DL) exhibits the ability to perform pattern recognition, image
processing, pattern learning, object detection, and pattern matching. Thus, it is a
valuable tool for early detection of tissue damage and human genome disorders
[1]. CAD is not a single platform; rather, it is a combination of intelligent
techniques like machine learning, machine vision, and medical imaging, forming
a hybrid model. It encompasses various techniques, including PET, MRI,
Dermoscopy, Biopsy, X-rays, mammography, ultrasound, and CT Imaging. Till
now, nearly one million images have been captured and analyzed.
CAD gained popularity in the analysis of stroke caused by brain injury and
abnormalities in abdominal images. It has the potential to translate blurry or
unclear medical images into high-contrast ones. Additionally, it can reconstruct
and enhance images or the region of interest (ROI). Moreover, CAD enables the
generation and tracking of medical reports for patients. These applications have
facilitated the detection of normal and chemically altered COVID-19 infections in
patients. Primarily, these procedures are utilized for the early detection of tumors
with the potential to develop cancerous tissues.
The remarkable output accuracy from AI, DL, and ML methods helps doctors and
patients by reducing the risk of exposure to hazardous ionizing radiation from
two-dimensional (2-D) and three-dimensional (3-D) imaging instruments [2].
APPLICATIONS OF CAD IN MEDICAL ANALYSIS
CAD approaches are helpful in recognising various diseases ranging from fever to
life-threatening diseases such as AIDS, cancer, Covid-19 (Fig. 1) outbreaks, heart
54 Disease Prediction using Machine Learning Irudaya Rani et al.
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attack, brain injury, skin abnormalities, and diabetic retinopathy. This tool is
effective in assessing chronic health conditions before the onset of visual
symptoms.
Fig. (1). Application of CAD tools in COVID-19 infection classification.
Cardiology Study using CAD
Echocardiography is the most used screening tool in cardiovascular medicine. It
has a simple data-capturing and interpretation mechanism. Thus, it can be
integrated with CAD to provide a cost-effective, radiation-free procedure.
Furthermore, 3-D imaging techniques such as CT and MRI are employed to give a
comprehensive heart anatomy.
CAD as shown in Fig. (2) can preprocess the echocardiography dataset and
provide superior output for echocardiographers. The field of CAD has recently
made significant progress and introduced the Echo Net, a deep neural network.
The model is capable of distinguishing cardiac structures and assessing their
activity. Echo Net also predicts systemic symptoms that may not be easily
recognized through probabilistic reasoning. Additionally, information echocar-
Computer-aided Bio-medical Tools Disease Prediction using Machine Learning 55
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diogram techniques, such as semi-supervised GANs, which excel at regression
tasks, have been developed to address access control issues.
TRADITIONAL PATHWAY
Surface Atlas
Statistical atlas of cardiac anatomy
60
40
20
0
-20
-40
-60
-60 -40 -30 0 20 40 60
z-scores
Cardiac Anatomy
Correction Network
CLASSIFICATION
True positive rate
0.70 0.75 0.80 0.85 0.90 0.95 1.00
0.0 0.2 0.4 0.6 0.8 1.0
False positive rate
SEGMENTATION MASKS
BIOPHYSICAL MODELS
Cardiac
Images
Metadata
Image analysis
Conversion to 3D
Volume Atlas
Misregistration
Short-axis
After motion
image stack
correction
Statistical Atlas Constrain Network
A Deep Neural Network
Architecture
Cardiac Images Feature Extraction Network
correction
Final model
Fitting
Image registration
+ Template mesh
MACHINE LEARNING PATHWAY
Fitted
surface
Fitted
mesh
Fig. (2). CAD tool supported cardiology inspection system.
Ophthalmology Study using CAD
In the field of ophthalmology, there has been a tremendous increase in AI-assisted
CAD efforts as demonstrated in Fig. (3). It is evident from the figure that clinical
diagnostic and computational powers of CAD exceed human ability [3]. Fundus
imaging and optical coherence tomography (OCT) are heavily utilized in this
system for examining and monitoring patients. From fundus pictures,
convolutional neural networks (CNNs) can accurately detect various diseases and
consistently assist physicians in decision making [4, 5]. Additionally, CNNs can
identify several non-human-interpretable features in the eyes, which suggest
relevant medical information.
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Fig. (3). CAD tool supported ophthalmology system for Glaucoma identification.
Fundus images have proven their utility in identification of various cardiovascular
and diabetes risk variables, including ageing, sexuality, drinking and smoking
habits, BMI, diastolic and systolic blood pressure etc. [6]. Furthermore, CNNs can
forecast the progression of center-involved glaucoma [7], age-related macular
degeneration [8], diabetic macular edema [9], and evident visual field loss [10],
among other conditions. Anemia [11] and chronic kidney disease [12] can also be
detected in fundus images. The emerging AI research, estimating non-ocular data
from eye scans, holds intriguing possibilities due to this discovery. It has the
potential to streamline treatment, as vision examinations can now be utilized to
check both ocular and non-ocular illnesses.
Dermatology Study using CAD
Lesion-specific differential diagnoses and detecting worrisome lesions amid
multiple benign lesions are two of the most important clinical challenges for CAD
in dermatology [13]. Numerous studies have demonstrated that CNNs can identify
normal and abnormal skin lesions with the same level of effectiveness as expert
dermatologists [14 - 18]. In fact, these studies have consistently shown
classification precision that is on par with, if not better than, that of doctors. This
method has evolved to include non-visual metadata as a categorization system,
enabling treatment options for a wide range of skin disorders, including nonneoplastic blemishes like dermatitis and genetic problems. These efforts have
been driven by open-access picture repositories and CAD campaigns that
encourage teams to compete based on specific standards.
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When these schemes are integrated into health services as shown in Fig. (4), they
can assist with various responsibilities, such as large-scale malignancy detection
and tracing lesions across images.
Input
Image
Extractor
Preprocessing
Stage
Deep Learning Features
Convolutional
Block
Extractor
Handcraft Features
h1, h2, . . . , h42, h
Convolutional
Block
Pooling
Block
43
Deep Learning
Features
d1 , d2 , . . . , d
Fusion Stage
The most informative features
d
d
,
,
1000
25
I(X;Y) = H(X) - H(X1Y)
, d
n-1
n
Input Images
Shape Color Texture
Handcraft Features
Feature
Extraction
Stage
Fig. (4). CAD methodology supported system for dermatology applications.
Pathology Study using CAD
h
, . . .
10
LR, SVM,
RVM
Classifier
Benign Malignant
Classification
Stage
Physicians play a critical role in cancer screening and therapy, which traditionally
involves evaluating tissue samples under a microscope. However, discrepancies in
diagnosis and prognosis may arise due to variations in visual perception and
clinical training [19]. Computer-Aided Diagnosis (CAD) can assist with various
tasks, including diagnostics, predicting treatment outcomes, oncology
segmentation, and diagnostic testing, particularly in mammography or X-ray
imaging, which is a common procedure for breast cancer screening [20, 21].
Early identification of breast cancer is crucial for improving the patient's quality
of life and emotional well-being. However, breast cancer screening images often
contain numerous abnormalities and disturbances, making it challenging to
identify and interpret cancer at its early stages (Fig. 5). To overcome this, it is
essential to standardize image quality and extract an ROI [22, 23]. CAD systems
analyze highly complex patterns in mammography images, although they may
increase false-positive rates. Nonetheless, they contribute to achieving high
58 Disease Prediction using Machine Learning Irudaya Rani et al.
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accuracy and sensitivity. Thus, may prove beneficial for mammography diagnosis
and patients.
Fig. (5). CAD supported pathology description to study breast cancer.
Currently, cancer detection utilizes submicron level precision cell probing with
high-efficient cameras [24]. This approach combines pathology with radiological,
genome-wide, and proteomic measurements, enhancing diagnosis and prognosis.
It also addresses visual information and cognitive function constraints, improving
the productivity and accuracy of routine tasks. Furthermore, it tackles sentient
visual interpretation and cognition limitations, leading to improved outcomes and
accuracy in routine tasks [25].
The system can identify and classify cells, nuclei, and mitoses, as well as locate
and segment anatomic elements like chromosomes, vesicles, glands, and
metastases. The current research focus lies in direct diagnoses [26, 27] and
prediction for various malignancies using tissue microarrays (TMA). Even
morphological traits collected through immune histo-chemistry H&E stain have
shown promise in predicting molecular biomarkers used in theragnosis [28, 29].
IMAGE PROCESSING METHODOLOGY ADOPTED IN CAD
Pre-processing
Each biological and medical diagnostic or imaging output contains a considerable
data volume that is time-intensive and inefficient to analyze if surgeons only
require a small portion of the image [30]. The data-shortening procedures utilizing
generic down sample and quantization techniques have been employed to reduce
the volume of medical information and improve the system’s performance.
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Additionally, a flattening data method or a regulatory strategy should be
implemented to reduce the dataset's susceptibility to noise, contrast, and size (Fig.
6). Smoothing techniques are also applied to reduce the risk of data loss [31].
Finally, the time required for prediction can be reduced using efficient preprocessing techniques.
Fig. (6). Schematic workflow showing the process happening in the Image Enhancement procedure.
Active Contour Method
The active contour technique removes impediments such as labels, patient
identities, scans, and taping artefacts using a thresholding-based automatic
algorithm. It also creates the shape for the muscle part’s boundary. Finally, it
combines the muscular part binary picture with the original image to get the
required upper chest muscle.
Seeded Region Growing Method
It is a colour image approach that focuses on the CLAHE procedure [32]. It uses a
unique seeded region growing (SRG) strategy to establish a compact rectangle
that distinguishes the musculature section of the ROI and then hides the tissue.
The technique of region expansion as shown in Fig. (7) combines pixels into
bigger patches based on established parameters [33]. The underlying plan is to
replace it with a seed point and then develop it into sections. For example, the
proposal of halting rules is a challenge in the seeded region-growing algorithm.
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The region must stop growing when there are no additional pixels that fulfill the
criteria for participation in that region.
Fig. (7). Seeded region growing technique.
The seeded region-growing algorithm’s steps are as follows:
Part 1: Begin reading from both the left top corners of the inverted right angle
triangle area.
Part 2: Calculate the selection technique by deducting the mean intensity from the
bitmap brightness and dividing by the difference between the estimated and mean
intensities.
Part 3: If the intensity is greater than zero and less than one, the pixel will be
fused to the growing area and the pixel values will be zero; otherwise, the
intensity value will stay intact.
(1)
The pixel intensity is denoted by P(x, y). Pavg, and Pmax, are average and
maximum intensity respectively. The region of the clipped visual that can be
enhanced will be confined to the area of the reversing right distance triangle. This
approach effectively removes the unwanted upper chest region from the image.
Morphological Operations
Erosion, dilation, closure, and opening as shown in Fig. (8) are the four basic
morphological processes. Erosion is employed to fill in the image’s gaps and
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holes. A dilated gradient mask is used to highlight the internal tumorous cells. The
feature vector disc is used with a radius of five in the image, beginning with
dilation, and ending with top hat and bottom hat transforms [34 - 36]. A
morphological open procedure is followed to eliminate the artifacts and
reconstruct the image.
Fig. (8). Morphological operations.
SEGMENTATION
Medical image segmentation as illustrated in Fig. (9) plays a significant role in
image analysis.
Segmentation methods divide a medical image into distinct regions based on
qualities such as brightness, color, texture, and reactivity. It helps to identify
regions of interest. In cancer imaging, segmentation plays a vital role in detecting
masses, micro-calcifications, and suspected lesions. It also aids in approximating
picture density by segmenting dense tissue sections. The precision of feature
assessment heavily relies on the quality of the segmented images [37, 38].
Image segmentation methods such as thresholding, region splitting, and region
growing algorithms as described below prove their efficacy in different types of
datasets [39].
Fig. (9). Fundus image segmentation using geodesic active contours.
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Edge Detection for Segmentation
Edge detection methods such as grey histograms and gradient-based methods
identify the pixels between various regions with increased productivity transitions
and form closed object boundaries [40]. Also, edge detection algorithms have the
potential to divide an image into normal and contoured segments.
Thresholding Method for Segmentation
Pixel-level segmentation is a simple and effective method for image segmentation
with the internal and external environment on a dark background. Grey
histograms and gradient-based methods. Thresholding methods select a suitable
threshold T to separate picture pixels into various areas and different objects from
the background. The adaptive thresholding technique turns a multilevel image into
a binary image. If the intensity of an image (x, y) is greater than or equal to a
preset threshold, i.e., f (x, y) ≥ T, the pixel is included as an image entity
otherwise, the pixel is part of the background [41]. There are two methods [42]
for selecting a thresholding value: global and local. The strategy is called global
thresholding when T is constant; otherwise, it is called local thresholding. Global
thresholding may fail when the backdrop illumination is uneven. To compensate
for uneven light, multiple thresholds are used in local thresholding [43]. These
techniques help in locating edge pixels while reducing noise. The three types of
image segmentation are edge-based, region-based, and blended thresholding.
Detection systems like the Canny edge detector and the Laplacian edge detector
can accept this type of region (Fig. 10). The Canny edge detector identifies
possible edge pixels using the gradient magnitude threshold and suppresses those
using non-maximal suppression and hysterics thresholding techniques. The
recognized edges consist of distinct pixels and may be incomplete or
discontinuous since these algorithms use pixel-based operations. As a result, postprocessing techniques, including morphological operations, are used to fill in the
gaps and join the breaks.
Fig. (10). Brain tumor segmented output.
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