Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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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: 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 Synthesis and Characterization of Pollen Extract Mediated Gold Nanostructures(Bingöl Üniversitesi Fen Bilimleri Enstitüsü, 2020) Bakar, Fatma; Sönmez, Hamide; Evecen, Senanur; Turan, Buse; Demir, Mehmet; Gümüş, Abdurrahman; Çeter, Talip; Yazgan, İdrisThere is an increasing demand in the synthesis of shape and size-controlled gold nanostructures (Au NSs) with greener methods. Therefore, we aimed to synthesize differently shaped and sized Au NSs using a greener technique under ambient conditions. In this study, we utilized pollen extracts of Corylus avellana, Juniperus oxycedrus and Pinus nigra species (collected from Kastamonu region of Turkey) for the synthesis. The extraction was performed in water in order to recover water soluble content from the pollen grains. The extracts were used to stabilize, and shape/size direct the HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer synthesized Au NSs. UV-vis, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) characterizations proved synthesis of spherical, anisotropic and large Au NSs with this benign approach. The obtained Au NSs were possible to separate small and large Au NSs through centrifugation. Chemistry of pollen extracts played key role on morphology and stability of the Au NSs. The findings, for the first time, is revealing the synthesis of large Au nanorod bundles (>300 nm) along with hexagonal and spherical Au NSs under ambient conditions using pollen grain extracts, whose maturation took 24h.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.