CNN derin öğrenme tekniği kullanılarak prostat kanser hastalığının teşhisi
Diagnosis of prostate cancer disease using CNN deep learning technique
Yazar: MEHMET EMİN SALMAN
Danışman: PROF. DR. İSMAİL HAKKI CEDİMOĞLU
Sakarya Üniversitesi / Fen Bilimleri Enstitüsü / Bilgisayar Mühendisliği Ana Bilim Dalı
Derece: Doktora, 2023
Konu: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol = Computer Engineering and Computer Science and Control ; Mühendislik Bilimleri = Engineering Sciences ; Patoloji = Pathology
Dizin: Bilgisayar destekli öğrenme = Computer assisted learning ; Makine öğrenmesi = Machine learning ; Prostat hastalıkları = Prostatic diseases ; Yapay Zeka Endeksi = Artificial Intelligence Index

Özet:
Prostat kanseri (PKa) için bir risk sınıflandırma yöntemi olan Gleason
derecelendirmesi subjektif, yani girdi görüntüyü inceleyen ve rapor eden patoloğun
deneyim ve uzmanlığına yoğun bir biçimde bağlıdır. Onun dışında, gleason
derecelendirmesi PKa teşhisi/tedavisi için önemli olsa da zaman alıcı bir iştir. Ayrıca,
biyopsi görüntülerini teşhis/derecelendirme süreci gözlemci patologlar arasında
önemli farklılıklara sahip olup, bireysel hastalar için teşhis/tedavi verimliliği ve
etkinliğini sınırlayabilmektedir.
Makine Öğrenmesi (MÖ) ve Derin Öğrenme (DÖ) sistemleri, gleason
derecelendirmesinin nesnelliğini ve verimliliğini artırma konusunda umut vaat
etmiştir. Ancak, derin öğrenme ağları tek başına kullanıldığında, eğitim verileri
dışındaki bir kaynaktan Tüm Slayt Görüntülerinde (TSG) etki alanı kayması ve düşük
performans sergiler. Dolayısıyla, bu çalışmada makine öğrenmesi ve derin öğrenme
algoritmalarının karma olarak kullanılması amaçlanmıştır. Bu sistemde, girdi biyopsi
görüntüsü üzerinde önemli bölgeleri otomatik olarak belirleyebilen ve belirlenen
bölgeleri doğru bir şekilde sınıflandırabilen yapay zekâ tabanlı prostat kanseri tespit
ve teşhis sistemi geliştirilmiştir. Önemli bölgeleri tespit etmek (yerelleştirme görevi
için) YOLO algoritması kullanılmıştır. Algoritma, bu çalışma için oluşturulan prostat
kanseri veri kümesi ile yeniden eğitilmiştir. Veri seti, 500 gerçek prostat dokusu
biyopsi görüntüsünden oluşmaktadır. Veri seti, veri artırma işleminden önce sırasıyla
450/50 gerçek prostat dokusu görüntüsü olarak eğitim/test kısımlarına ayrılmıştır.
Daha sonra 450 etiketli biyopsi görüntüsünden oluşan eğitim seti, veri çoğaltma
yöntemi ile ön işleme tabi tutulmuştur. Bu sayede veri setindeki biyopsi görüntü sayısı
450'den 1776'ya yükseltilmiştir. Ardından veri seti ile algoritma eğitilmiş ve otomatik
prostat kanseri tespit ve teşhis aracı geliştirilmiştir.
Geliştirilen araç iki test seti ile test edilmiştir. İlk test seti, eğitim setine benzeyen 50
görüntü içermektedir. Böylece %97 tespit ve sınıflandırma doğruluğu sağlanmıştır.
İkinci test seti ise tamamen farklı 137 gerçek prostat doku biyopsi görüntüsü içerir.
Böylece %89 tespit doğruluğu elde edilmiştir. Bu çalışmada, otomatik prostat kanseri
tespit ve teşhis aracı geliştirilmiştir. Test sonuçları, nesne algılama algoritmaları gibi
yapay zekâ (bilgisayar görüşü) yöntemleri kullanılarak yüksek doğruluklu (yüksek
performanslı) prostat kanseri teşhis araçlarının geliştirilebileceğini göstermektedir. Bu
sistemlerin, patologlar arasındaki gözlemciler arası değişkenliği azaltabileceği ve tanı
aşamasındaki zaman gecikmesini önlemeye yardımcı olabileleceği kanısına
varılmıştır.

Abstract:
The most precious thing in the realm of existence is life. And the most valuable thing
among duties is to serve life. Therefore, it is certain that the health sector has a very
important place in our lives. With the advancement of technology, many studies have
been and are being carried out to help doctors and bring the health sector to a more
advanced level. The biggest thing that affects human health is getting cancer.
Unfortunately, many people all over the world and in our country die due to cancer.
According to the statistics, lung cancer is known as the most common type of cancer
in the world today. Prostate cancer comes second.
The risk of developing cancer is one of the leading health problems in Turkey as well
as all over the world. According to the Turkish Cancer Statistics data of the Ministry
of Health, prostate cancer ranks second among the top 10 cancer types in 2015. When
considered according to all age groups, prostate cancer ranks second with 12.9%,
13.2% when considered according to 50-69 age groups, and 18.3% when considered
according to age groups of 70 and above.
According to the results of the research, it is obvious that determining the causes of
the disease and taking measures against cancer in order to protect people from cancer,
which affects human health the most, will contribute greatly to human health and the
future of the country. Considering these great goals, it is seen that it has become
mandatory for the information technologies and the health services community to work
together. This is why interdisciplinary work has an important place in the fight against
cancer. Working between disciplines not only saves time but also enables a more
accurate diagnosis and treatment to be made.
Detecting prostate cancer is a difficult and time-consuming task performed manually
by pathologists. Diagnosis of pathologists according to the degree of disease; may vary
according to personal experience, volume of practice and natural subjectivities. When
diagnosing the disease in prostate cancer, our biggest predictor is to determine the
gleason score and tumor stage. The Gleason grading system is considered part of a
standard protocol when evaluating prostate histopathological specimens (biopsy).
Grading is based on tumor appearance and architectural patterns shown in prostate
tissue samples in glands. The rating score ranges from 1 to 5.
The development of methods based on digital image processing and machine learning
to classify tissues and determine the degree of disease, as well as predict the outcome
of disease and automatically analyze pathology images, has become popular recently.
Computer-assisted pathology has made it easier for pathologists to diagnose tissue
samples. The deep learning method, which is a supervised learning technique by
extracting automatic features from the data, where machine learning and classical
probabilistic and statistical methods are insufficient, has become quite common in
diagnosing many types of cancer.
The CNN architecture of deep learning consists of three layers: the input layer, the convolution layers, and the fully connected classification layer. CNN architecture; It consists of widely used object detection and classification sub-architectures such as RCNN, FR-CNN, DenseNet-121, ResNet-50, ImageNet, VGG-16, MobileNet SSD, Inception-V3 and YOLO.
There are many studies using CNN architecture algorithms for the diagnosis of prostate
cancer disease. The fact that these studies obtained different results from each other is
due to the fact that the model is different, the data set has a different set, and the amount
of data set of the trained system is low. 
In the past, the accuracy of the systems in the detection and diagnosis of diseases using CNN techniques was generally below 60%. Therefore, the difference and low accuracy rate of these studies is due to many factors and problems such as the lack of object detection part of some methods, the small data set and the training process. Methods such as MobileNet SSD, DenseNet-121, ResNet-50, FR-CNN, R-CNN do not scan the entire input image, but focus only on randomly selected regions. As a
result, these methods may miss important regions. This may result in misclassification. However, the YOLO object detection method detects and classifies objects or regions by scanning an entire input image. For this, the YOLO method has a very powerful structure in detecting objects or desired regions.
The first of the reasons why our study differs from other studies is that there is not a
new method with the YOLO method and there is no study conducted with YOLO in
the diagnosis of prostate cancer disease so far. The second major difference is that
these models, together with the previous probabilistic and metaheuristic algorithm
methods, diagnose prostate cancer with very few data sets in the diagnosis of disease.
In our study, 1776 datasets were used. 
The subject of this thesis is to find the position of healthy or unhealthy tissues in an input biopsy image and to classify the found region into classes such as benign, grade 3, grade 4, and grade 5 by grading with the Gleason score. We performed these
operations by retraining the YOLO-based general-purpose object detection algorithm
with our own dataset samples. As a result, an automated prostate biopsy image
processing and diagnostic tool was developed. We realized the performance and
accuracy values of this developed system by using complex matrix (confusion matrix)
and accuracy (accuracy) measurement methods.
In our system, the YOLO algorithm was retrained with 1776 prostate tissue pattern
images obtained from 450 real patient tissue images. Relevant sites were labeled by
one pathologist and confirmed by two different pathologists. The performance of the
system was measured with two test sets. Test set 1 consists of 50 biopsy images similar
to the images in the training set. Test set 2, on the other hand, consists of completely
different biopsy images from the images in the trained system.
All images in test set 1 and test set 2 were carefully reviewed by three pathologists. As
a result, an average of 97% accuracy was obtained for test set 1 and an average of 89%
accuracy for test set 2.The results of the system show that an artificial intelligence system based on the YOLO object detection algorithm can achieve successful detection and classification
results between benign biopsy nuclei and nuclei containing malignancy. It can also be
concluded that an artificial intelligence system can grade prostate biopsies with high
performance.
Increasing the number of samples in order to shed light on future studies may yield
more positive results as it will increase the reliability and performance of the system.
In addition, better learning of the cellular properties of the system by going down to
the core details of the tissues with closer photos can increase the performance and
accuracy of the system.