Citation: Amin, J.; Anjum, M.A.;
Sharif, M.; Kadry, S.; Nadeem, A.;
Ahmad, S.F. Liver Tumor
Localization Based on YOLOv3 and
3D-Semantic Segmentation Using
Deep Neural Networks. Diagnostics
2022, 12, 823. https://doi.org/
10.3390/diagnostics12040823
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 2 February 2022
Accepted: 22 March 2022
Published: 27 March 2022
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Article
Liver Tumor Localization Based on YOLOv3 and 3D-Semantic
Segmentation Using Deep Neural Networks
Javaria Amin
1
, Muhammad Almas Anjum
2
, Muhammad Sharif
3
, Seifedine Kadry
4,
* , Ahmed Nadeem
5
and Sheikh F. Ahmad
5
1
Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan; javeria.amin@uow.edu.pk
2
National University of Technology (NUTECH), Islamabad 44000, Pakistan; almasanjum@yahoo.com
3
Department of Computer Science, Comsats University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan;
muhammadsharifmalik@yahoo.com
4
Department of Applied Data Science, Noroff University College, 4609 Kristiansand, Norway
5
Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455,
Riyadh 11451, Saudi Arabia; anadeem@ksu.edu.sa (A.N.); fashaikh@ksu.edu.sa (S.F.A.)
* Correspondence: seifedine.kadry@noroff.no
Abstract:
Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use
of computed tomography (CT) for early detection of liver cancer could save millions of lives per year.
There is also an urgent need for a computerized method to interpret, detect and analyze CT scans
reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task
because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of
the liver. To address these concerns, a model comprised of three parts: synthetic image generation,
localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is
utilized for generation of synthetic images. The generated images are localized by using the improved
localization model, in which deep features are extracted from pre-trained Resnet-50 models and
fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the
minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in
which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is
trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the
proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven
that, in comparison to the recently published work in this domain, this research has localized and
segmented the liver and minute liver tumor with more accuracy.
Keywords:
generative adversarial network; deeplabv3; inceptionresnetv2; YOLOv3; ResNet-50;
liver tumor
1. Introduction
The main organ, situated behind the right ribs and beneath the base of the lung, is
the liver, which helps in food digestion [
1
]. It is responsible for filtering of the blood cells,
nutritional recovery, and storage [
2
]. The two major areas of the liver are the right and left
lobes. The caudate & quadrate are further two types of lobes. The liver cells grow rapidly
and may spread to other areas of the body, which is similar to the cause of hepatocellular
carcinoma (HCC) [
3
]. Hepatic primary malignancies arise when the cells have irregular
actions [
4
]. In 2008, 750,000 liver cancer patients were diagnosed, 696,000 of whom died
because of it [
5
]. In 2021, 42,230 cases of liver tumor/cancer including were diagnosed,
12,340 women & 29,890 men, 30,230 of which died (9930 female and 20,300 male) [
6
]. Glob-
ally the prevalence of infection among males is approximately double that of females [
7
,
8
].
Medical, imaging [
9
,
10
], and laboratory studies, such as MRI scans, and CT scans, detect
primary liver malignancy [
11
]. To obtain accurate images from different angles such as
the axial, coronal, and sagittal slices, a CT scan uses radiation [
12
]. Hepatic malignancy
Diagnostics 2022, 12, 823. https://doi.org/10.3390/diagnostics12040823 https://www.mdpi.com/journal/diagnostics
