Atropa belladonna is one of the herbs used to treat wounds and prevent inflammation. This study provides a scientific assessment for the wound healing potential of biodegradable nanofibers containing Ag nanoparticles encapsulated with atropa belladonna extract (eAgNPs). Ultraviolet-visible (UV-vis) spectroscopy was used to observe the localized surface plasmon resonance (LSPR) band of AgNPs synthesized from atropa belladonna extract prepared under different conditions. Polycaprolactone (PCL) nanofibers with eAgNPs were fabricated using the electrospinning technique. The distribution of AgNPs and eAgNPs and the size of nanofibers were characterized with scanning and transmission electron microscopy (SEM, TEM) before and after degradation at the end of 18 weeks. Fourier transform infrared (FTIR) spectroscopy showed the surface interactivity between AgNPs, atropa belladonna extract and PCL nanofibers and also approved the modification of PCL nanofibers with eAgNPs. X-ray diffraction analysis (XRD) defined the formation of the crystalline AgNPs and appreciated the orientation of the nanofibers. Results of tension tests revealed that modification of PCL nanofibers with pure AgNPs and eAgNPs significantly increased strength and tensile modulus. Due to the hydrophobic nature of PCL, modification with pure AgNPs and eAgNPs slightly reduced its hydrophobicity. Biodegradation tests of PCL nanofibers with eAgNPs exhibited a higher degradation rate than neat PCL nanofibers. In vitro MTT results revealed that eAgNPs doped PCL samples have better cell viability than AgNPs doped and neat PCL nanofibers. Owing to their antibacterial properties, biodegradation rates, low cytotoxicity, mechanical and surface morphologic properties of AgNPs modified PCL nanofibers containing atropa belladonna are considered to have a great potential for skin regeneration.

Breast cancer is one of the five most common types of cancer that occurs when breast tissue turns into a tumor and mainly affects women. Early diagnosis of the disease is crucial for the patient's lifespan. However, misclassification of malignancy may result in treatment delays and initiate an irreversible process for the patient. This study proposes an approach for classifying ultrasound breast images into malignant, benign, and healthy categories, with a particular emphasis on minimizing false-negative outcomes. The BUSI dataset, characterized by imbalanced class distributions, was used for the breast cancer detection. The dataset was augmented to enhance feature representations using contrast-limited adaptive histogram equalization (CLAHE) to address the class imbalance issue, creating the BUSICL dataset. Features extracted from both datasets with the VGG16 and ResNet50 models were then classified using a support vector machine (SVM). Following the results analysis, the SVM algorithm's cost matrix values were adjusted according to the inverse proportions of class distributions applying a cost-sensitive approach. In addition, the robustness of the proposed methodology is compared with the K-Means SMOTE algorithm. The proposed method achieved an overall accuracy of 99.36%, surpassing the performance of previous comprehensive classification studies using the BUSI dataset.

Alzheimer's Disease is the most common type of dementia and is a progressive, neurodegenerative disease. The disease worsens over time, and the patient becomes bedridden, unable to move or understand what is happening around him. The main concern of medicine is to slow down the progression of the disease for which no treatment has yet been developed. Artificial intelligence studies have achieved significant success in detecting many diseases. In this study, an artificial intelligence-based approach that uses MR images of the early stage of Alzheimer's Disease to detect the disease at an early stage is presented. Initially, a new dataset was created through the application of the fuzzy technique, thereby expanding the feature space. Then, an ensemble learning-based hybrid deep learning model was developed to reduce the misclassification rate for all classes. The features derived from the inception module, residual modules, and histogram of oriented gradients descriptor are subjected to classification through bagging and boosting algorithms. The proposed model has surpassed many state-of-the-art studies by achieving a high success rate of 99.60% in detecting Alzheimer's disease in its early stages.

Kolorektal kanser tanısında kullanılan histopatolojik görüntüler, içerik tabanlı medikal görüntü geri getirme (CBMIR) sistemleriyle desteklenerek klinik süreçlerin etkinliğini artırabilir. Bu çalışmada, düşük parametreli ve hesaplama açısından verimli derin öğrenme mimarilerine dayanan CBMIR_SMEr adlı bir yöntem önerilmektedir. Yöntem, histopatolojik kolorektal görüntülere yönelik bir CBMIR yaklaşımı olarak EBHI-Seg veri seti üzerinde uygulanmıştır. ShuffleNet, MobileNetV2 ve EfficientNet-B0 mimarilerinden çıkarılan öznitelikler birleştirilmiş, ardından ReliefF algoritmasıyla en ayırt edici öznitelikler seçilmiştir. Görüntü benzerlikleri Öklidyen (Euclidean) ve Kosinüs (Cosine) ölçüm yöntemleriyle hesaplanmıştır. EBHI-Seg veri seti üzerinde, Kosinüs benzerliği kullanıldığında 800 öznitelik için mAP@5 = 0.9511 ve Precision@5 = 0.9141, Öklidyen benzerliği kullanıldığında ise 800 öznitelik için mAP@5 = 0.9353 ve 600 öznitelik için Precision@5 = 0.9109 değerleri elde edilmiştir. Yöntemin kararlılığı UC Merced görüntü veri kümesi üzerinde test edilmiş, Öklidyen benzerliği ile 600 öznitelikte mAP@5 = 0.9461 ve 800 öznitelikte Precision@5 = 0.9259, Kosinüs benzerliği ile 600 öznitelikte mAP@5 = 0.9577 ve 800 öznitelikte Precision@5 = 0.9340 değerleri elde edilmiştir. Bu sonuçlar, her iki veri kümesi için de elde edilen en yüksek geri getirme performanslarını göstermektedir. Deneysel bulgular, önerilen CBMIR_SMEr yönteminin hem histopatolojik hem de genel görüntü veri kümelerinde yüksek doğruluk ve kararlılıkla çalıştığını ortaya koymakta ve kolorektal görüntü analizi alanında literatüre önemli bir katkı sağlamaktadır.