Okur, Erdem
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Okur E.
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erdem.okur@ieu.edu.tr
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05.04. Software Engineering
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17
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12
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61
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61
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97
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| Journal | Count |
|---|---|
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 2 |
| 2024 Medical Technologies Congress -- OCT 10-12, 2024 -- Bodrum, TURKIYE | 2 |
| 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 | 1 |
| 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 | 1 |
| Engıneerıng Applıcatıons of Artıfıcıal Intellıgence | 1 |
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Conference Object Dermoscopic Lesion Segmentation Via Optimal Color Channel Fusion(IEEE, 2024) Okur, Erdem; Unay, Devrim; Turkan, MehmetDeath caused by various kinds of cancer is on rise and skin cancer is one of the most common one worldwide. Due to the importance of early detection, dermoscopy is adopted for visualizing skin lesions and computer-aided detection benefits from these dermoscopic images for better diagnosis results. One of the most important phase of automated skin lesion detection or classification is segmentation, but it is a very challenging task because of several artifacts existing on these images. In this paper, a new method to improve skin lesion segmentation from the existing deep network architectures is proposed, based on the fusion of various results produced by existing models on different color channels. Experimental validations demonstrate that the proposed method increases the average accuracy, on lesion segmentation in terms of Sorensen-Dice and Jaccard indices, when compared to conventional techniques.Conference Object Citation - Scopus: 1Patch Enhancement for Melanoma Detection With Bag of Visual Words(IEEE, 2022) Okur, Erdem; Turkan, MehmetMelanoma is a type of skin cancer caused by the ultraviolet radiation of Sun. Melanoma will become severe if it is not detected early, and it may spread to other body organs, most commonly the lungs, brain, liver, and bones. Dermatologists look for tell-tale signs of melanoma on pigmented skin lesions (moles) to detect it or, in some cases, differentiate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in a series of biopsies that are unnecessary. Furthermore, this type of imprecision may allow a melanoma case to spread undetected. This study develops an automatic melanoma detection system to overcome this challenge. The proposed method is based on Bag of Visual Words (BoVW) with a new patch enhancement scheme, which incorporates both traditional and cutting-edge methods. Experimental comparisons between the proposed method and the well-known convolutional neural network models demonstrate the effectiveness of the developed system.Master Thesis An Assignment Algorithm for Nurses Using Hl7(İzmir Ekonomi Üniversitesi, 2015) Okur, Erdem; Ünlütürk, Mehmet Süleyman; Kılıç, GökhanHer gün artan sayılarla bir çok hasta hastanelere geliyor. Bazıları kontrolleri için, bazılarıysa daha farklı ve belki de daha ciddi problemlerle. Durumlarına göre bazen hastalar, bir ya da bir kaç gün hastanede kalmak zorunda kalabiliyorlar. Hemşire çizelgesi Problemi tam da bu noktada ortaya çıkıyor. Hemşireler, hastanede kalan hastalardan bütün gün sorumlu. Tabi ki hastane tarafında bu hastalar bir iş yükü oluşturmakta ve bu yük hemşirelere eşit olarak dağıtılmalı. Bu işlem baş hemşire tarafından gerçekleştiriliyor. Ne yazık ki böyle bir dağıtım, hemşireler arasında her zaman eşit iş yükü sağlayamıyor. Bazı durumlarda hastaların bulunduğu odalar paylaştırılıyor ancak bu odaların bazılarında tek hasta varken, bazılarındaysa 4 ila 6 hasta olabiliyor. Dahası bu hastaların durumları ve ihtiyaçları birbirinden farklı. Bu tezde iş yükü dağılımını otomatik ve daha eşit hale getirmek için tasarlanan bir algoritma sunuluyor. Bu algoritma, hastaları bulundukları yatak bilgisi ve ihtiyaç durumlarını göz önünde bulundurarak hemşirelere dağıtacaktır.Conference Object Citation - Scopus: 3Multiword Expression Detection in Turkish Using Linguistic Features(Institute of Electrical and Electronics Engineers Inc., 2017) Metin, S.K.; Taze M.; Uymaz, H.A.; Okur E.Detection of multiword expressions is an important pre-task in several research topics such as natural language understanding, automatic text summarization, and machine translation in the area of natural language processing. In this study, detection of multiword expressions in Turkish texts is accepted as a classification problem. 6 types of linguistic features are defined solving this problem in Turkish texts. The classification tests are performed by 10 different classifiers utilizing the prepared data set. The performance of classifiers is measured for different sizes of random train-test sets by running the tests 10 times. The test results showed that linguistic features can be used in identification of multiword expressions. And it is observed that SMO and J48 algorithms reached the highest classification performances based on different evaluation metrics. © 2017 IEEE.Article Citation - WoS: 3Citation - Scopus: 2Weighted Bag of Visual Words With Enhanced Deep Features for Melanoma Detection(Pergamon-Elsevier Science Ltd, 2024) Okur, Erdem; Türkan, MehmetThe human skin, the largest organ with multiple layered functionalities, houses melanocytes in the deeper strata of its epidermis. These cells can be adversely impacted by ultraviolet radiation, thereby instigating melanoma, the deadliest form of skin cancer. Failure to detect melanoma at an early stage can potentially lead to metastasis, forming complex tumors in other tissues. Despite substantial efforts, visual inspections can occasionally overlook melanoma cases due to inherent subjectivity. To surmount this challenge, an automated detection system is necessary. Recent attempts to establish such a system have predominantly employed push-throughstrategies involving deep (neural) networks and their ensembles, which however necessitate significant computational resources. This paper presents a novel approach, amalgamating a conventional machine learning technique, Bag of Visual Words, with a pretrained deep neural network for comprehensive deep feature extraction from enhanced input image patches. The proposed method, assessed on the ISIC Challenge 2017 dataset, surpassed all other entries on the challenge leader-board, registering an accuracy of 96.2% in the task of lesion classification.Conference Object Citation - Scopus: 1Melanoma Detection in Dermoscopic Images: a Bag of Visual Words Approach(IEEE, 2022) Okur, Erdem; Turkan, MehmetMelanoma is a skin cancer caused by the ultraviolet radiation from the sun. If it is not detected at early stages, melanoma will become severe and more importantly it may spread to the other body organs, most commonly to the lungs, brain, liver and bones. Dermatologists look for the tell tales on the pigmented lesions (moles) on the skin to detect melanoma, or for some cases discriminate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in the form of a series of biopsies which maybe not needed. Furthermore, this type of imprecision may allow a melanoma case to grow without a notice. To overcome this challenge, an automatic melanoma detection system is proposed in this study. The developed approach is based on Bag of Visual Words (BoVW) which includes both traditional and new age methods. Experimental comparisons between this novel approach and well-known convolutional neural network models show the effectiveness of the proposed model.Article Citation - WoS: 56Citation - Scopus: 86A Survey on Automated Melanoma Detection(Pergamon-Elsevier Science Ltd, 2018) Okur, Erdem; Turkan, MehmetSkin cancer is defined as the rapid growth of skin cells due to DNA damage that cannot be repaired. Melanoma is one of the deadliest types of skin cancer, which originates from melanocytes. While other skin cancer types have limited spreading capabilities, the danger of melanoma comes from its ability to spread (metastasize) rapidly. Fortunately, it can be detected by visual inspection of the skin surface, and it is 100% curable if identified in the early stages. However, detection by subjective visual inspection creates an important problem, due to investigators' different levels of experiences and education. Dermoscopy (dermatoscopy) has significantly increased the diagnostic accuracy of melanoma since late 90's. In addition, several systems have been proposed in order to assist investigators or to perform an automatic melanoma detection. This survey focuses on the algorithms for automated melanoma detection in dermoscopic images through an extensive analysis of the stages in methodologies proposed in the literature, and by examining related concepts and describing possible future directions through open problems in this domain of research.Conference Object A Comparative Study on Skin Cancer Detection: Multi-Class Vs. Binary(IEEE, 2024) Basut, Sudenaz; Kurtbas, Yagmur; Guler, Nilay; Okur, Erdem; Turkan, MehmetSkin cancer, particularly melanoma, is a major public health concern due to its high fatality rate. Early diagnosis is crucial for improving patient outcomes, and advances in computer-aided diagnostic systems based on deep learning have showed promise in increasing diagnostic accuracy. This study examines two methods for handling the multi-class classification issue in skin cancer diagnosis. The first strategy utilizes a single EfficientNet-b0 model to classify all classes at once, whereas the second approach, that can be thought as "waterfall" method, employs a sequence of binary classifiers, each designed to detect one specific class at a time. Both techniques were evaluated on the ISIC 2018 dataset, and the findings show that the waterfall like strategy improves classification accuracy by around 8%. This study illustrates the potential benefits of sequential binary classification in dealing with complicated multi-class problems in medical image analysis especially for skin cancer; nevertheless, more research with other metrics is required to corroborate these findings and explore different network models.Conference Object Citation - WoS: 2Citation - Scopus: 4Deep Learning Based Melanoma Detection From Dermoscopic Images(Institute of Electrical and Electronics Engineers Inc., 2019) Berkay M.; Mergen E.H.; Binici R.C.; Bayhan Y.; Gungor A.; Okur E.; Unay D.; Türkan, MehmetMelanoma which occurs with non-healing DNA degradation in melanocyte cells, is the most deadly type of skin cancers. Importantly, it can be identified for a treatment before it spreads to other tissues, i.e., early diagnosis. To identify, a specialist visually inspects whether the suspected lesion is melanoma or not. However, due to different education and experience levels of specialists or as a result of the patient not being in a facility that is specialized to this area, the problem of 'subjectivity' arises, and a good visual investigation accuracy may not always be achieved. Therefore, there is a significant need for automatic detection tools and systems. In this study, a method based on deep learning for automatic detection of melanoma from dermoscopic images is proposed. The developed system is tested with a large dataset and encouraging results are obtained. © 2019 IEEE.Doctoral Thesis Automated melanoma detection in dermoscopic images(İzmir Ekonomi Üniversitesi, 2023) Okur, Erdem; Türkan, MehmetKanser, çeşitli ve tespit edilmesi zor türleri ile insanlar için en tehlikeli hastalıklardan biri haline gelmiştir. Melanom, türleri arasında ölüm oranı en fazla olan cilt kanseri türüdür. Olağan melanom tespit süreci, hastanın farkındalığına ve görsel muayene eden kişinin deneyimine dayanmaktadır. Dermoskopların icadı ile etkileri azalsa da, "öznellik" sorunu melanom tespit doğruluğunda büyük rol oynamakta ve bu da otomatik algılama ihtiyacını doğurmaktadır. Bu tezde, dermoskopik görüntülerde otomatik melanom tespitinin tarihçesi ve daha önce sunulan sistemlerin açıkları incelenmiştir. Bu açıkların üstesinden gelmek için farklı yaklaşımlar araştırılmıştır. Sonuç olarak, geleneksel yöntemleri yeni çağın derin öğrenme teknikleriyle birleştiren Görsel Kelimeler Çantası (BoVW) konseptine dayalı bir melanom saptama algoritması oluşturulmuştur. Yeni algoritmanın performansı, popüler Uluslararası Cilt Görüntüleme İşbirliği (ISIC) 2017 yarışması veri kümesi üzerinde test edilmiş ve son derece iyi sonuçlar elde edilmiştir. %96,2 doğrulukla ve daha da önemli olarak %99,8 hassasiyetle yeni algoritma ISIC 2017 başarı tablosundaki diğer tüm katılımcıları geride bırakmıştır. Hassasiyet, algoritmanın melanom vakalarını doğru sınıflandırma konusundaki başarısını temsil ettiğinden bu başarı, algoritmayı alanında özel bir yere yerleştirmektedir. Son olarak, yeni doğan algoritmanın performansını daha da arttırmak açısından, alan üzerinde gelecekte izlenebilecek yönler araştırılmıştır.