Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 5Citation - Scopus: 7Adaptive Resizer-Based Transfer Learning Framework for the Diagnosis of Breast Cancer Using Histopathology Images(Springer, 2023) Düzyel, Okan; Çatal, Mehmet Sergen; Kayan, Ceyhun Efe; Sevinç, Arda; Gümüş, AbdurrahmanBreast cancer is a major global health concern, and early and accurate diagnosis is crucial for effective treatment. Recent advancements in computer-assisted prediction models have facilitated diagnosis and prognosis using high-resolution histopathology images, which provide detailed information on cancerous tissue. However, these high-resolution images often require resizing, leading to potential data loss. In this study, we demonstrate the effect of a learnable adaptive resizer for breast cancer classification using the BreakHis dataset. Our approach incorporates the adaptive resizer with various convolutional neural network models, including VGG16, VGG19, MobileNetV2, InceptionResnetV2, DenseNet121, DenseNet201, and EfficientNetB0. Despite producing visually less appealing images, the learnable resizer effectively improves classification performance. DenseNet201, when jointly trained with the adaptive resizer, achieves the highest accuracy of 98.96% for input images of 448x448 resolution. Our experimental results demonstrate that the adaptive resizer performs better at a magnification factor of 40x compared to higher magnifications. While its effectiveness becomes less pronounced as image resolution increases to 100x, 200x, and 400x, the adaptive resizer still outperforms bilinear interpolation. In conclusion, this study highlights the potential of adaptive resizers in enhancing performance for medical image classification. By outperforming traditional image resizing methods, our work contributes to the advancement of deep neural networks in the field of breast cancer diagnostics.Article Citation - WoS: 1Citation - Scopus: 1Real-Time Superficial Vein Imaging System for Observing Abnormalities on Vascular Structures(Springer, 2023) Altay, Ayşe; Gümüş, AbdurrahmanCirculatory system abnormalities might be an indicator of diseases or tissue damage. Early detection of vascular abnormalities might have an important role during treatment and also raise the patient's awareness. Current detection methods for vascular imaging are high-cost, invasive, and mostly radiation-based. In this study, a low-cost and portable microcomputer-based tool has been developed as a Near-Infrared (NIR) superficial vascular imaging device. The device uses NIR Light-Emitting Diode (LED) light at 850 nm along with other electronic and optical components. It operates as a non-contact and safe infrared (IR) imaging method in real-time. Image and video analysis are carried out using OpenCV (Open-Source Computer Vision), a library of programming functions mainly used in computer vision. Various tests were carried out to optimize the imaging system and set up a suitable external environment. To test the performance of the device, the images taken from three diabetic volunteers, who are expected to have abnormalities in the vascular structure due to the possibility of deformation caused by high glucose levels in the blood, were compared with the images taken from two non-diabetic volunteers. As a result, tortuosity was observed successfully in the superficial vascular structures, where the results need to be interpreted by the medical experts in the field to understand the underlying reasons. Although this study is an engineering study and does not have an intention to diagnose any diseases, the developed system here might assist healthcare personnel in early diagnosis and treatment follow-up for vascular structures and may enable further opportunities.Article Citation - WoS: 9Citation - Scopus: 12Intensity and Phase Stacked Analysis of a 40-Otdr System Using Deep Transfer Learning and Recurrent Neural Networks(Optica Publishing Group, 2023) Kayan, Ceyhun Efe; Yüksel Aldoğan, Kıvılcım; Gümüş, AbdurrahmanDistributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phasesensitive optical time domain reflectometer (40-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the 40-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications. (c) 2023 Optica Publishing GroupArticle Citation - WoS: 18Citation - Scopus: 23On the Effect of Modified Carbohydrates on the Size and Shape of Gold and Silver Nanostructures(MDPI Multidisciplinary Digital Publishing Institute, 2020) Yazgan, İdris; Gümüş, Abdurrahman; Gökkuş, Kutalmış; Demir, Mehmet Ali; Evecen, Senanur; Sönmez, Hamide Ayçin; Toprak, Muhammet S.Gold (Au) and silver (Ag) nanostructures have widespread utilization from biomedicine to materials science. Therefore, their synthesis with control of their morphology and surface chemistry have been among the hot topics over the last decades. Here, we introduce a new approach relying on sugar derivatives that work as reducing, stabilizing, and capping agents in the synthesis of Au and Ag nanostructures. These sugar derivatives are utilized alone and as mixture, resulting in spherical, spheroid, trigonal, polygonic, and star-like morphologies. The synthesis approach was further tested in the presence of acetate and dimethylamine as size- and shape-directing agents. With the use of transmission electron microscopy (TEM), selected area electron diffraction (SAED), x-ray diffraction (XRD), scanning electron microscopy (SEM), and ultraviolet-visible (UV-vis) absorption spectroscopy techniques, the particle size, shape, assembly, aggregation, and film formation characteristics were evaluated. NPs' attributes were shown to be tunable by manipulating the sugar ligand selection and sugar ligand/metal-ion ratio. For instance, with an imine side group and changing the sugar moiety from cellobiose to lactose, the morphology of the Ag nanoparticles (NPs) transformed from well dispersed cubic to rough and aggregated. The introduction of acetate and dimethylamine further extended the growth pattern and morphological properties of these NPs. As examples, L5 AS, G5AS, and S5AS ligands formed spherical or sheet-like structures when used alone, which upon the use of these additives transformed into larger multicore and rough NPs, revealing their significant effect on the NP morphology. Selected samples were tested for their stability against protein corona formation and ionic strength, where a high chemical stability and resistance to protein coating were observed. The findings show a promising, benign approach for the synthesis of shape- and size-directed Au and Ag nanostructures, along with a selection of the chemistry of carbohydrate-derivatives that can open new windows for their applications.Article Citation - WoS: 11Citation - Scopus: 12Expandable Polymer Assisted Wearable Personalized Medicinal Platform(Wiley, 2020) Babatain, Wedyan; Wicaksono, Irmandy; Buttner, Ulrich; El-atab, Nazek; Rehman, Mutee Ur; Hussain, Muhammad Mustafa; Gümüş, AbdurrahmanConventional healthcare, thoughts of treatment, and practice of medicine largely rely on the traditional concept of one size fits all. Personalized medicine is an emerging therapeutic approach that aims to develop a therapeutic technique that provides tailor-made therapy based on everyone's individual needs by delivering the right drug at the right time with the right amount of dosage. Advancement in technologies such as wearable biosensors, point-of-care diagnostics, microfluidics, and artificial intelligence can enable the realization of effective personalized therapy. However, currently, there is a lack of a personalized minimally invasive wearable closed-loop drug delivery system that is continuous, automated, conformal to the skin, and cost-effective. Here, design, fabrication, optimization, and application of a personalized medicinal platform augmented with flexible biosensors, heaters, expandable actuator and processing units powered by a lightweight battery are shown. The platform provides precise drug delivery and preparation with spatiotemporal control over the administered dose as a response to real-time physiological changes of the individual. The system is conformal to the skin, and the drug is transdermally administered through an integrated microneedle. The developed platform is fabricated using rapid, cost-effective techniques that are independent of advanced microfabrication facilities to expand its applications to low-resource environments.Article Citation - WoS: 59Citation - Scopus: 57Cmos Enabled Microfluidic Systems for Healthcare Based Applications(John Wiley and Sons Inc., 2018) Hussian, Muhammad M.; Khan, Sherjeel M.; Gümüş, Abdurrahman; Nassar, Joanna M.With the increased global population, it is more important than ever to expand accessibility to affordable personalized healthcare. In this context, a seamless integration of microfluidic technology for bioanalysis and drug delivery and complementary metal oxide semiconductor (CMOS) technology enabled data-management circuitry is critical. Therefore, here, the fundamentals, integration aspects, and applications of CMOS-enabled microfluidic systems for affordable personalized healthcare systems are presented. Critical components, like sensors, actuators, and their fabrication and packaging, are discussed and reviewed in detail. With the emergence of the Internet-of-Things and the upcoming Internet-of-Everything for a people–process–data–device connected world, now is the time to take CMOS-enabled microfluidics technology to as many people as possible. There is enormous potential for microfluidic technologies in affordable healthcare for everyone, and CMOS technology will play a major role in making that happen.Article Citation - WoS: 6Citation - Scopus: 10Surface Chemistry Dependent Toxicity of Inorganic Nanostructure Glycoconjugates on Bacterial Cells and Cancer Cell Lines(Elsevier, 2023) Sancak, Sedanur; Yazgan, İdris; Bayarslan, Aslı Uğurlu; Ayna, Adnan; Evecen, Senanur; Taşdelen, Zehra; Gümüş, Abdurrahman; Sönmez, Hamide Ayçin; Demir, Mehmet Ali; Demir, Sosin; Bakar, Fatma; Dilek Tepe, HafizeSurface functionalized nanostructures have outstanding potential in biological applications owing to their target-specific design. In this study, we utilized laboratory synthesized carbohydrate-derivatives (i.e., galactose, mannose, lactose, and cellobiose derivatives) for aqueous one-pot synthesis of gold (Au) and silver (Ag) nanostructure glycoconjugates (NSs), and iron metal-organic framework glycoconjugates (FeMOFs). This work aims to test whether differences in the surface chemistry of the inorganic nanostructures play roles in revealing their toxicities towards bacterial cells and cancerous cell lines. As of the first step, biological activity of AuNSs, AgNSs, and FeMOFs were tested against a variety of gram (−) and gram (+) bacterial strains, where AgNSs possessed moderate to high antibacterial activities against all the tested bacterial strains, while AuNSs and FeMOFs showed their bacterial toxicity mostly depending on the strain. Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) determination studies were performed for the nanostructure glycoconjugates, for which μg/mL MBC values were obtained such as (Cellobiose p-aminobenzoic acid_AgNS) CBpAB_AgNS gave 50 μg/mL MBC value for P.aeruginosa and S.kentucy. The activity of selected sugar ligands and corresponding glycoconjugates were further tested on MDA-MB-231 breast cancer and A549 lung cancer cell lines, where selective anticancer activity was observed depending on the surface chemistry as well. Besides, D-penicillamine was introduced to galectin specific sugar ligand coated AuNS glycoconjugates, which showed very strong anticancer activities even at low doses. Overall, the importance of this work is that the surface chemistry of the inorganic nanostructures can be critical to reveal their toxicity towards bacterial cells and cancerous cell lines.