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  1. Article ; Online: Multi-model CNN fusion for sperm morphology analysis.

    Yüzkat, Mecit / Ilhan, Hamza Osman / Aydin, Nizamettin

    Computers in biology and medicine

    2021  Volume 137, Page(s) 104790

    Abstract: Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility of ... ...

    Abstract Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility of sperm are important characteristics evaluated by experts during semen analysis. Most laboratories perform the tests manually. However, manual semen analysis requires much time and is subject to observer variability during the evaluation. Therefore, computer-assisted systems are required. Additionally, to obtain more objective results, a large amount of data is necessary. Deep learning networks, which have become popular in recent years, are used for processing and analysing such quantities of data. Convolutional neural networks (CNNs) are a class of deep learning algorithm that are used extensively for processing and analysing images. In this study, six different CNN models were created for completely automating the morphological classification of sperm images. Additionally, two decision-level fusion techniques namely hard-voting and soft-voting were applied over these CNNs. To evaluate the performance of the proposed approach, three publicly available sperm morphology data sets were used in the experimental tests. For an objective analysis, a cross-validation technique was applied by dividing the data sets into five sub-sets. In addition, various data augmentation scales and mini-batch analysis were employed to obtain the highest classification accuracies. Finally, in the classification, accuracies 90.73%, 85.18% and 71.91% were obtained for the SMIDS, HuSHeM and SCIAN-Morpho data sets, respectively, using the soft-voting based fusion approach over the six created CNN models. The results suggested that the proposed approach could automatically classify as well as achieve high success in three different data sets.
    MeSH term(s) Algorithms ; Cell Count ; Humans ; Male ; Neural Networks, Computer ; Semen Analysis ; Spermatozoa
    Language English
    Publishing date 2021-08-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2021.104790
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  2. Article ; Online: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets.

    Ilhan, Hamza Osman / Serbes, Gorkem / Aydin, Nizamettin

    Applied intelligence (Dordrecht, Netherlands)

    2021  Volume 52, Issue 8, Page(s) 8551–8571

    Abstract: The Coronavirus disease (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe ... ...

    Abstract The Coronavirus disease (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples taken by the nasopharyngeal swab technique. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs) and ensemble learning idea, which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using seven CNN architectures were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.
    Language English
    Publishing date 2021-10-30
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-021-02945-8
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  3. Article ; Online: Automated sperm morphology analysis approach using a directional masking technique.

    Ilhan, Hamza Osman / Serbes, Gorkem / Aydin, Nizamettin

    Computers in biology and medicine

    2020  Volume 122, Page(s) 103845

    Abstract: Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi- ...

    Abstract Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi-stage cascade connected preprocessing techniques, region based descriptor features, and non-linear kernel SVM based learning is proposed for the classification of any stained sperm images for the assessment of the morphology. The proposed framework was evaluated on two sperm morphology datasets: the Human Sperm Head Morphology dataset (HuSHeM) and Sperm Morphology Image Data Set (SMIDS). The results indicate that cascading the preprocessing techniques used in the proposed framework, such as wavelet based local adaptive de-noising, modified overlapping group shrinkage, image gradient, and automatic directional masking, increased the classification accuracy by 10% and 5% for the HuSHeM and SMIDS, respectively. The proposed framework results in better overall accuracy than most state-of-the-art methods, while having significant advantages, such as eliminating the exhaustive manual orientation and cropping operations of the competitors with reasonable rates of consumption of time and source.
    MeSH term(s) Cell Count ; Humans ; Male ; Semen Analysis ; Sperm Head ; Spermatozoa
    Language English
    Publishing date 2020-06-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2020.103845
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  4. Article: Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets

    Ilhan, Hamza Osman / Serbes, Gorkem / Aydin, Nizamettin

    Abstract: The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe ... ...

    Abstract The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs), which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using CNNs were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  5. Book ; Online: Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets

    Ilhan, Hamza Osman / Serbes, Gorkem / Aydin, Nizamettin

    2020  

    Abstract: The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe ... ...

    Abstract The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs), which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using CNNs were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.

    Comment: 20 Pages, 9 Figures, 4 Tables and submitted a journal
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-11-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Sperm characteristics of wild‐caught and hatchery‐reared turbot, Scophthalmus maximus, originated from the Black Sea

    Aydin, İlhan / Öztürk, Rafet Çağrı / Polat, Hamza / Beken, Atife Tuba / Terzi, Yahya / Özel, Osman Tolga / Erbay, Esen Alp / Düzgüneş, Zehra Duygu / Altuntaş, Ayça / Küçük, Ercan

    Journal of applied ichthyology. 2022 Jan., v. 38, no. 1

    2022  

    Abstract: In this study sperm characteristics (sperm volume, sperm volume per kg fish, spermatocrit, sperm concentration, pH, and sperm motility) of wild‐caught and hatchery‐reared turbot (Scophthalmus maximus), originated from the Black Sea population, were ... ...

    Abstract In this study sperm characteristics (sperm volume, sperm volume per kg fish, spermatocrit, sperm concentration, pH, and sperm motility) of wild‐caught and hatchery‐reared turbot (Scophthalmus maximus), originated from the Black Sea population, were assessed. In this regard, two different trials were conducted. On the Trial‐I, sperm characteristics of wild‐caught and hatchery‐reared turbot (4‐, 7‐, and 10‐year‐old) were comparatively investigated during the spawning season (May), on the Trial‐II, monthly variations of sperm characteristics of hatchery‐reared turbot (4‐year‐old) were investigated. On the Trial‐I, a positive correlation between body weight and sperm volume was recorded. Significant differences were noted in sperm volume, and sperm concentration, however, there was no significant difference between the groups in terms of sperm volume per kg of body weight, spermatocrit, and pH. The sperm motility showed a significant decrease after the 15 minutes post‐activation in all the specimens. The active spermatozoa rate of the wild‐caught turbot was 44% at 15 minutes post‐activation, whereas it was 16%, 13%, and 64% for 4‐, 7‐, and 10‐year‐old turbot, respectively. The average motility duration of the wild‐caught turbot spermatozoa was significantly longer compared to hatchery‐reared turbot despite having a comparatively lower initial motility rate than hatchery‐reared turbot. On Trial‐II spermiation duration of hatchery reared Black Sea turbot was determined as 6 months, starting from February until the end of July. The highest and the lowest sperm volume, and sperm volume per kg were recorded in June and July, respectively. Significant differences were determined between the months in terms of sperm volume per kg, sperm volume, spermatocrit, and sperm concentration, however, the pH was similar. These results demonstrate the sperm characteristics of wild‐caught and hatchery‐reared Black Sea turbot were different in the spawning season (May) and the potential spermiation season was between February and July.
    Keywords Scophthalmus maximus ; body weight ; hatcheries ; ichthyology ; pH ; sperm concentration ; sperm motility ; spermiation ; turbot ; Black Sea
    Language English
    Dates of publication 2022-01
    Size p. 73-83.
    Publishing place John Wiley & Sons, Ltd
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 283875-8
    ISSN 0175-8659
    ISSN 0175-8659
    DOI 10.1111/jai.14272
    Database NAL-Catalogue (AGRICOLA)

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  7. Article ; Online: Comparing Informative Sample Selection Strategies in Classification Ensembles

    Hamza Osman İlhan / Mehmet Fatih Amasyal

    International Journal of Machine Learning and Computing, Vol 4, Iss 1, Pp 79-

    2014  Volume 84

    Abstract: Usage of more training data with label information gives more success for classification of datasets in machine learning. But in real life, obtaining data with label information is a cost-effective and long-lasting process. Herein, active learning ... ...

    Abstract Usage of more training data with label information gives more success for classification of datasets in machine learning. But in real life, obtaining data with label information is a cost-effective and long-lasting process. Herein, active learning algorithms are emerged. Active learning algorithms aim to maintain current success rate with fewer samples in train set or increase total success of model in training process. Active learning is not only functional for regular learning methods but also can be used in ensemble learning algorithms with specified techniques. In this study, two different active learning algorithms based on class probabilities of the samples are tested on five datasets classification. Ensemble learning methods are used as classification model. Comparative results presented as graphically and numerically.
    Keywords Active learning ; adaboost ; bagging ; decision tree ; ensemble learning ; machine learning. ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Computer Science ; DOAJ:Technology and Engineering
    Language English
    Publishing date 2014-02-01T00:00:00Z
    Document type Article ; Online
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  8. Article ; Online: Comparing Informative Sample Selection Strategies in Classification Ensembles

    Hamza Osman İlhan / Mehmet Fatih Amasyal

    International Journal of Machine Learning and Computing, Vol 4, Iss 1, Pp 79-

    2014  Volume 84

    Abstract: Usage of more training data with label information gives more success for classification of datasets in machine learning. But in real life, obtaining data with label information is a cost-effective and long-lasting process. Herein, active learning ... ...

    Abstract Usage of more training data with label information gives more success for classification of datasets in machine learning. But in real life, obtaining data with label information is a cost-effective and long-lasting process. Herein, active learning algorithms are emerged. Active learning algorithms aim to maintain current success rate with fewer samples in train set or increase total success of model in training process. Active learning is not only functional for regular learning methods but also can be used in ensemble learning algorithms with specified techniques. In this study, two different active learning algorithms based on class probabilities of the samples are tested on five datasets classification. Ensemble learning methods are used as classification model. Comparative results presented as graphically and numerically.
    Keywords Active learning ; adaboost ; bagging ; decision tree ; ensemble learning ; machine learning. ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Computer Science ; DOAJ:Technology and Engineering
    Language English
    Publishing date 2014-02-01T00:00:00Z
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Clinical Characteristics and Outcomes of COVID-19 in Turkish Patients with Hematological Malignancies

    Civriz Bozdağ, Sinem / Cengiz Seval, Güldane / Yönal Hindilerden, İpek / Hindilerden, Fehmi / Andıç, Neslihan / Baydar, Mustafa / Aydın Kaynar, Lale / Toprak, Selami Koçak / Göksoy, Hasan Sami / Balık Aydın, Berrin / Demirci, Ufuk / Can, Ferda / Özkocaman, Vildan / Gündüz, Eren / Güven, Zeynep Tuğba / Özkurt, Zübeyde Nur / Demircioğlu, Sinan / Beksaç, Meral / İnce, İdris /
    Yılmaz, Umut / Eroğlu Küçükdiler, Hilal / Abishov, Elgün / Yavuz, Boran / Ataş, Ünal / Mutlu, Yaşa Gül / Baş, Volkan / Özkalemkaş, Fahir / Üsküdar Teke, Hava / Gürsoy, Vildan / Çelik, Serhat / Çiftçiler, Rafiye / Yağcı, Münci / Topçuoğlu, Pervin / Çeneli, Özcan / Abbasov, Hamza / Selim, Cem / Ar, Muhlis Cem / Yücel, Orhan Kemal / Sadri, Sevil / Albayrak, Canan / Demir, Ahmet Muzaffer / Güler, Nil / Keklik, Muzaffer / Terzi, Hatice / Doğan, Ali / Yegin, Zeynep Arzu / Kurt Yüksel, Meltem / Sadri, Soğol / Yavaşoğlu, İrfan / Beköz, Hüseyin Saffet / Aksu, Tekin / Maral, Senem / Erol, Veysel / Kaynar, Leylagül / İlhan, Osman / Bolaman, Ali Zahit / Sevindik, Ömür Gökmen / Akyay, Arzu / Özcan, Muhit / Gürman, Günhan / Ünal, Şule / Yavuz, Yasemin / Diz Küçükkaya, Reyhan / Özsan, Güner Hayri

    Turkish journal of haematology : official journal of Turkish Society of Haematology

    2021  Volume 39, Issue 1, Page(s) 43–54

    Abstract: Objective: Patients with solid malignancies are more vulnerable to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection than the healthy population. The outcome of SARS-CoV-2 infection in highly immunosuppressed populations, such as in ...

    Abstract Objective: Patients with solid malignancies are more vulnerable to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection than the healthy population. The outcome of SARS-CoV-2 infection in highly immunosuppressed populations, such as in patients with hematological malignancies, is a point of interest. We aimed to analyze the symptoms, complications, intensive care unit admissions, and mortality rates of patients with hematological malignancies infected with SARS-CoV-2 in Turkey.
    Materials and methods: In this multicenter study, we included 340 adult and pediatric patients diagnosed with SARS-CoV-2 from March to November 2020. Diagnosis and status of primary disease, treatment schedules for hematological malignancies, time from last treatment, life expectancy related to the hematological disease, and comorbidities were recorded, together with data regarding symptoms, treatment, and outcome of SARS-CoV-2 infection.
    Results: Forty four patients were asymptomatic at diagnosis of SARS-CoV- 2 infection. Among symptomatic patients, fever, cough, and dyspnea were observed in 62.6%, 48.8%, and 41.8%, respectively. Sixty-nine (20%) patients had mild SARS-CoV-2 disease, whereas moderate, severe, and critical disease was reported in 101 (29%), 71 (20%), and 55 (16%) patients, respectively. Of the entire cohort, 251 (73.8%) patients were hospitalized for SARS-CoV-2. Mortality related to SARS-CoV-2 infection was 26.5% in the entire cohort; this comprised 4.4% of those patients with mild disease, 12.4% of those with moderate disease, and 83% of those with severe or critical disease. Active hematological disease, lower life expectancy related to primary hematological disease, neutropenia at diagnosis of SARS-CoV-2, ICU admission, and first-line therapy used for coronavirus disease-2019 treatment were found to be related to higher mortality rates. Treatments with hydroxychloroquine alone or in combination with azithromycin were associated with a higher rate of mortality in comparison to favipiravir use.
    Conclusion: Patients with hematological malignancy infected with SARS-CoV-2 have an increased risk of severe disease and mortality.
    MeSH term(s) Adult ; Amides/administration & dosage ; Azithromycin/administration & dosage ; COVID-19/complications ; COVID-19/mortality ; Child ; Hematologic Neoplasms/complications ; Hematologic Neoplasms/mortality ; Hematologic Neoplasms/therapy ; Humans ; Hydroxychloroquine/administration & dosage ; Hydroxychloroquine/adverse effects ; Pyrazines/administration & dosage ; SARS-CoV-2 ; Turkey/epidemiology
    Chemical Substances Amides ; Pyrazines ; Hydroxychloroquine (4QWG6N8QKH) ; Azithromycin (83905-01-5) ; favipiravir (EW5GL2X7E0)
    Language English
    Publishing date 2021-09-15
    Publishing country Turkey
    Document type Journal Article ; Multicenter Study
    ZDB-ID 2185903-6
    ISSN 1308-5263 ; 1300-7777
    ISSN (online) 1308-5263
    ISSN 1300-7777
    DOI 10.4274/tjh.galenos.2021.2021.0287
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