Serkan Savaş

Bio: Serkan Savaş is an academic researcher from Gazi University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 5, co-authored 11 publications receiving 60 citations. Previous affiliations of Serkan Savaş include Çankırı Karatekin University.

Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures

Abstract: Deep learning algorithms have begun to be used in medical image processing studies, especially in the last decade. MRI is used in the diagnosis of Alzheimer’s disease, a type of dementia disease, which is the 7th among the diseases that cause death in the world. Alzheimer’s disease has no known cure in the literature, so it is important to attempt treatment before starting the irreversible path by diagnosing the pre-illness stages. In this study, the previous stages of Alzheimer’s disease were classified as normal, mild cognitive impairment, and Alzheimer’s disease through brain MRIs. Different models using CNN architecture were used to classify 2182 image objects obtained from the ADNI database. The study was presented in a very comprehensive comparison framework, and the performances of 29 different pre-trained models on images were evaluated. The accuracy values of each model and the precision, specificity, and sensitivity rates of each class were determined. In the study, the EfficientNetB0 model provided the highest accuracy at the test stage with an accuracy rate of 92.98%. In the comparative evaluation stage with the confusion matrix, the highest rates of precision, sensitivity, and specificity values of the Alzheimer’s disease class were achieved by EfficientNetB3 (89.78%), EfficientNetB2 (94.42%), and EfficientNetB3 (97.28%) models, respectively. The results of the study showed that among the pre-trained models, EfficientNet models achieved a high rate of classification performance as the models with the highest performance. This study will contribute to clinical studies in early prevention by detecting Alzheimer’s disease before it occurs.

Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

Abstract: Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification.

Veri madenciliği ve Türkiye’deki uygulama örnekleri

Abstract: Gunumuz teknolojisi hizla ilerlemekte ve her gecen gun gucu de artmaktadir. Bilgisayarlarin bilgi saklama kapasitelerinin artmasiyla birlikte bilgi kaydi yapilan alanlarin sayisi da artmaktadir. Bundan dolayi eldeki verilerin analizi ve sonucu bu verilerden kestirme yontemlerinin onemi karar vericiler icin gittikce artmaktadir. Bilgisayar sistemleri ile uretilen veriler tek baslarina degersizdir, cunku ciplak gozle bakildiginda bir anlam ifade etmezler. Bu veriler belli bir amac dogrultusunda islendigi zaman bir anlam ifade etmeye baslar. Bu yuzden buyuk miktardaki verileri isleyebilen teknikleri kullanabilmek buyuk onem kazanmaktadir. Bu ham veriyi bilgiye veya anlamli hale donusturme islemleri veri madenciligi ile yapilabilmektedir. Bu calismada veri madenciliginin gunumuz disiplinleri arasinda geldigi noktaya deginilmis ve Turkiye’de veri madenciligi uzerine yapilan calismalar ve gerceklestirilen uygulamalar incelenmistir

Data analysis through social media according to the classified crime

Abstract: The amount and variety of data generated through social media sites has increased along with the widespread use of social media sites. In addition, the data production rate has increased in the same way. The inclusion of personal information within these data makes it important to process the data and reach meaningful information within it. This process can be called intelligence and this meaningful information may be for commercial, academic, or security purposes. An example application is developed in this study for intelligence on Twitter. Crimes in Turkey are classified according to Turkish Statistical Institute criminal data and keywords are defined according to this data. A total of 150,000 tweet data in the Turkish language are collected from Twitter between specified dates and processed by Turkish Zemberek natural language processing. It is seen that 56 % of the people are talking about terrorist attacks and bombing attacks on the study dates. The words "bomb", "terror", "attack", "organization", and "explode" have percentages of 24 %, 12 %, 8 %, 6 %, and 6 %, respectively. Moreover, associations between words and situations are found. Correlations are important to create new subclusters like "terror" and "rape" in this study with 0.90 correlation. Bigger masses can be accessible by expanding keyword groups to have a clear picture of the real situation.

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review.

Abstract: The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Abstract: Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images

Abstract: Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ( $\Delta$ TPA), Pearson correlation coefficient ( r ) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3–85.7%, and algorithm TPAs were strongly correlated ( r = 0.985–0.988; p $^2$ using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated ( r = 0.972, p $\Delta$ TPA = −0.44 $\pm$ 4.05 mm $^2$ and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.