Thoracic Diseases Classification for Chest X-Ray
Images using Deep Learning Techniques
Sarath Chandra Gollamudi
School of Computing
National College of Ireland
Dublin
x19193581@student.ncirl.ie
Mansi Udani
School of Computing
National College of Ireland
Dublin
x19186088@student.ncirl.ie
I. MOTIVATION AND RESEARCH QUESTION
A. Motivation
In 2oth century, the world is experiencing the usefulness
of Machine Learning which is an application of Artificial
Intelligence. It helps the computers to take the decision on
their own and to find the underlying patterns in the data by
training with the machine learning algorithms. On the other
hand, Deep Learning is the subfield of that which helps for
analysing a large amount of unstructured and structured data.
The deep learning algorithms are introduced with inspiration
by the structure and functioning of the brain which is called
artificial neural networks. There are different deep learning
algorithms which can be used for different kind of datasets.
The main motivation for choosing this topic is that chest x-ray
exams are most cost effective medical examinations. To
minimise the cost, we could use the machine learning
algorithms by training the models with the real time images.
On the other hand, the medical industry is one of the high
priority sectors and people need the highest level of care and
this data being analysed by experts. After solving the realworld problems successfully by implementing deep learning
techniques which provides exciting solutions and accurate
results in the medical industry. In this paper, we have used the
deep learning CNN algorithm for the analysis.
B. Research Question
To predict and classify the diseases using the chest x-ray
images with deep learning techniques.
II. INITIAL LITERATURE REVIEW
Chest X-ray (CXR) is a conventional medical imaging
technology essentially used for diagnosing thoracic diseases
like lung nodule, pneumonia, atelectasis and many more. The
effortless and speedy feature of CXR makes it popular choice
for clinical examinations. Reading X-rays can be tricky as it
is error-prone and requires an expertise. Automatic detection
of diseases using deep learning approaches has provided great
value [1].
In radiology, the most conducted exams are on the Chest
radiographs which helps for the detection of various diseases
related to the high mortality. In this paper [2] Yaniv Bar with
his colleagues examines the detection of pathology in chest
radiographs using machine learning algorithms, which are
SVM and CNN (Convolutional Neural Network) with the
dataset consist of 433 images. The results showed that CNN
performs better over SVM with an AUC of 0.87 to 0.94 for
the different pathologies.
Wu et al. [3] proposes an automatic approach to save time
and cost by labelling findings on chest x- rays. This task is
Shashikumar Madihalli Byrappa
School of Computing
Natioanl College of Ireland
Dublin
x18173837@student.ncirl.ie
performed manually by radiologists to detect finding
locations. To address this problem, automatic labeling of
chest x-ray images is done by leveraging radiology reports.
The first stage involves lung segmentation of normal patient
to mark six lung zones with standardized bounding boxes.
The second stage involves labeling each lung zone positive
or negative by associating it to the radiology report. Using
this approach, an average annotation precision of 0.896 and
recall of 0.881 is evaluated.
In the world population, people are suffering from many
diseases, the TB (Tuberculosis) is one of them caused by a
Mycobacterium. In the paper [4] Wai Yan Nyein Naing, Zaw
Z. Htike performed research on detecting TB using different
machine learning algorithms SVM, Fuzzy Logic, and ANN on
different datasets of chest x-rays. The results showed that the
accuracy rate of SVM is 78.3% and 84% respectively for the
first and second datasets. On the other hand, there were 50%
correct classifications for the image’s dataset.
For the examination of cancer disease detection of mitosis
in breast cancer histology images is very complex and time is
taken process. In the paper [5] D. C. Ciresan with his
colleagues has performed research to automate and make it
robust in the detection process using deep learning technique
DNN (Deep max-pooling convolutional neural network). The
results showed that the precision, recall, and F1 score are with
0.88, 0.70, and 0.782, respectively.
In recent years Deep learning is widely used in the medical
field to classify the medical signals or images. Convolutional
Neural Networks are broadly used for image classification
problems. In this study classification of chest X-ray images,
we used CNN for its high performance [6].
Similarly, Wang et al. [7] explore neural networks along with
external medical knowledge to construct a novel method
called knowledge-guided deep zoom neural network
(KGZNet). The disease classification focuses to gradually
zoom on discriminative regions from coarse to fine. A Lung
region generator captures lung region areas where thoracic
diseases occur. Then a lesion region generator augments
focus on lesion area in lung regions. Finally, disease
classification is obtained by fusing features model with
medical discriminative features knowledge. The dataset
achieves an average AUC of 0.878.
Earlier image diagnosis is one of the biggest problems in
the medical field. Deep learning methods facilitate doctors to
overcome this problem by classifying the images to
corresponding diseases and treat them to the corresponding
diseases. In this paper, we used Multiple Convolutional
Neural Networks to detect the abnormal problem using chest
X-ray images [8].
X-ray images are most used to identify heart diseases.
Understanding of chest radiographs for doctors is one of the
crucial and deciding factors to detect the different diseases like
fibrosis, heart, tuberculosis, and many other diseases, also it is
a time-consuming and challenging task for the medical field.
Hence computer techniques are used to get accurate results. In
this paper, we implemented Deep Convolutional Neural
Network to classification the chest X-ray into 14 different
diseases with ensuring accurate diagnosis [9].
III. DATASET USED
NIH Chest X-ray dataset consists of 112,120 frontal
view x-ray images in the PNG format. Each image contains
14 different thorax disease labels (absent or present) and a
data enquiry CSV file from 30,805 unique patients. This
dataset is taken from the Kaggle repository.
Link: https://www.kaggle.com/nih-chest-xrays/data
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