Introduction and Aim: Primary mediastinal B-cell lymphomas (PMBL) are aggressive B- cell lymphomas. Although the initial treatment models vary in PMBL, appropriate treatment methods are not known. We ...aim to show real-life data on health outcomes in adult patients with PMBL who received various type of chemoimmunotherapies in Turkey.
Method: We analyzed the data of 61 patients who received treatments for PMBL from 2010 to 2020. The overall response rate (ORR), overall survival (OS) and progression-free survival (PFS) of the patients were evaluated.
Results: 61 patients were observed in this study. The mean age of the study group was 38.4 ± 13.5 years. From among them, 49.2% of the patients were female (n = 30). For first-line therapy, 33 of them had received rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) regimen (54%). Twenty-five patients had received rituximab, etoposide, prednisone, vincristine, cyclophosphamide and doxorubicin (DA-EPOCH-R) regimen. The ORR was 77%. The median OS and PFS were as follows: 25 months (95% CI: 20.4-29.4) and 13 months (95% CI: 8.6-17.3), respectively. The OS and PFS at 12 months were 91.3% and 50%, respectively. The OS and PFS at five years were 64.9% and 36.7%, respectively. Median follow-up time period was 20 months (IQR 8.5-38.5).
Conclusion: R-CHOP and DA-EPOCH-R showed good results in PMBL. These remain one of the best determined systemic treatment options for first-line therapy. Also, the treatment was associated with good efficacy and tolerability.
The process of identifying apple varieties holds pivotal importance in pomology and agricultural science. This intricate task not only aids growers in optimizing orchard management, but also ...profoundly impacts consumers and the apple industry as a whole. Selecting the right apple varieties tailored to specific environmental conditions and market demands is instrumental for the sustainability and economic viability of apple cultivation. Accurate apple variety identification further contributes to maintaining product quality and ensuring consumer satisfaction. Traditional identification methods, however, are susceptible to human error given the vast diversity of apple cultivars. In response, the integration of advanced technologies, including image processing and machine learning, has emerged as a promising approach to enhance accuracy and efficiency in apple variety identification, benefitting both the agricultural and commercial sectors. The classification of apple types involved feature extraction using three methods: MobileNetV2, EfficientNetV2B0, and a combination of GLCM and Color-Space algorithms from apple images. Machine learning models were then built to classify apple varieties, utilizing various algorithms such as support vector machine (SVM), k-nearest neighbors (Knn), random subspace (RSS), and random forest. In the case of "EfficientNetV2B0 + GLCM + Color-Space" and utilizing the ReliefF feature selection method, the random forest algorithm attains peak performance with an accuracy, precision, recall, and F-score all registering an impressive 98.33%.
Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach ...involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.
Increasingly, more effective breeding techniques for new variations are preferred due to population growth and climatic change, particularly the accurate identification of the target variety. Maize ...haploid breeding technology, which can shorten the reproductive period and improve germplasm, has become the key to new maize breeding. In this study, a method in which deep features and image patches are analyzed together was proposed using a dataset consisting of 3000 different haploid/diploid type maize seed images in total. To achieve this objective, we adopted convolutional neural networks (CNNs) to recognize haploid and diploid maize seeds automatically through a transfer learning approach. More specifically, DenseNet201, ResNet152, ResNetRS50, RegNetX002, EfficientNetV2B0, EfficientB0, EfficientB1, EfficientB2, EfficientB3, EfficientB4, EfficientB5, EfficientB6, and EfficientB7 were applied for this specific task. The proposed hybrid model is inspired by both transfer learning and vision transformers. The error, accuracy, f1-score, recall, precision, and AUC of hybrid proposed model were 0.1491, 0.9633, and 0.9712, respectively. The accuracy rate reached, and the proposed model requires less processing in terms of complexity, which reveals the need for further investigation of such hybrid models. On the other hand, with the results obtained, it has been revealed that the maize seeds can be separated as haploid and diploid with traditional methods can be done much faster and without the need for an expert decision.
The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this ...study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors.
Glaucoma is an eye disease that spreads over time without showing any symptoms at an early age and can result in vision loss in advanced ages. The most critical issue in this disease is to detect the ...symptoms of the disease at an early age. Various researches are carried out on machine learning approaches that will provide support to the expert for this diagnosis. The activation function plays a pivotal role in deep learning models, as it introduces nonlinearity, enabling neural networks to learn complex patterns and relationships within data, thus facilitating accurate predictions and effective feature representations. In this study, it is focused on developing an activation function that can be used in CNN architectures using glaucoma disease datasets. The developed function (Trish) was compared with ReLU, LReLU, Mish, Swish, Smish, and Logish activation functions using SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax, and Nadam optimizers in CNN architectures. Datasets consisting of retinal fundus images named ACRIMA and HRF were used within the scope of the experiments. These datasets are widely known and currently used in the literature. To strengthen the test validity, the proposed function was also tested on the CIFAR-10 dataset. As a result of the study, 97.22% validation accuracy performance was obtained. It should be stated that the acquired performance value is at a significant level for the detection of glaucoma.
•A high-performance and efficient system for detect type of otitis media.•Hyper parameter optimization of deep learning model.•Proposed model can assist the otolaryngologist to make accurate ...diagnosis.•A novel deep learning model for classification of tympanic membrane conditions.
Middle ear health is a process that generally depends on eardrum health. Middle ear disorders are more common during childhood. Permanent damage may occur in bacterial or viral infections in this region if an early diagnosis is not made. Infectious ear disease, especially known as Otitis Media, is one of the diseases. In this study, morphological features of images are obtained by using various feature extraction methods. Deep feature-based transfer learning and hyperparameter optimization methods were used to detect the presence and type of otitis media. While the EfficientNet convolutional neural network (CNN) model was used to extract deep features, KNN, SVM, and Ensemble classifiers were used as classifiers. Bayesian, Grid Search, and Random Search were used for hyperparameter optimization. As a result of the experiments carried out, it was observed that the classification performance was 99.1%.
Early diagnosis of plant diseases is one of the key elements determining plant productivity. The productivity and quality of plants are significantly reduced when plant diseases are not identified ...and prevented in a timely manner, which results in major financial losses for producers. Olive is a plant with high added value. While the fruit and oil of olive are consumed as food, its oil is used in cosmetics, medicine, etc. It is also used in industries. In addition, active substances such as oleuropein, triterpene, maslinic acid, and flavonoid found in olive leaves are also used in the pharmaceutical industry. Considering all these valuable uses of olive, the importance of productivity is understood. Plant diseases are one of the most significant factors affecting the yield of olives. Among these diseases, fungal disease called peacock eye can spread to the whole tree through the leaves. This disease causes reduced crop production, defoliation, and rot of tree branches. In this study, an efficient method was developed to detect peacock eye disease from olive leaves. In the first stage, an original dataset of healthy and diseased leaves was created. Then, by extracting deep features from this dataset with CNN models, diseased and healthy leaf classification was performed with the transfer learning approach. As a result of the experiments, very satisfactory results were obtained around 98.63%.
Early diagnosis of cancer allows for easy follow-up of patients’ treatment processes. The utilization of microarray gene technology has become increasingly prevalent in the detection of cancer. ...However, the limited success of classical data mining methods can be attributed to the absence of a linear relationship among the data in microarray datasets. Artificial intelligence-based classification methods are employed to address classification challenges in datasets with a high number of attributes, due to the following reasons: This study presents a novel approach that combines adaptive particle swarm optimization (PSO) with the artificial bee colony (ABC) algorithm to effectively classify microarray datasets. Firstly, Chi2 and ANOVA F-test feature selection algorithms are applied to the microarray datasets to prevent the model from getting stuck in the local optimum. Thus, the most defining features in the dataset are selected, and the dimension is reduced. For classification success increases, we have selected optimal features with the proposed hybrid adaptive ABC + PSO, gray wolf algorithm (GWO), PSO, and ABC algorithms on the new dataset obtained. The last step is performed to classify using a support vector machine (SVM), an artificial neural network (ANN), and a nearest-neighbor (k-NN) algorithm. In the study, eight microarray datasets liver, lung, renal, pancreatic, prostate, breast, colorectal, and brain tumors were used.
people’s COVID-19 knowledge and HL levels are thought to be effective in managing the pandemic. This study aimed to evaluate candidate soldiers’ HL and COVID-19 knowledge levels in the Altındağ ...district of Ankara, Turkey. Material and Methods A questionnaire form containing socio-demographic characteristics, 16-item European HL Survey Questionnaire, and propositions about COVID-19 was applied to candidate soldiers Who were referred for COVID-19 PCR sampling to the Altindg District Health Directorate before enlistment between December 2021_April 2022. Results and Discussion The study was completed with 668 candidate soldiers, most of whom were young adults. HL level of 20.5% was inadequate. The frequency of those who had not been vaccinated against COVID-19 was 16.8%. The COVID-19 knowledge level of those vaccinated was higher (p=0.002). The propositions about COVID-19 symptoms and correct mask use were answered correctly at the highest rate in COVID-19 knowledge level questions. The COVID-19 knowledge level score was significantly higher in those with adequate HL levels, aged 25 and over, non-smokers, and who had university or higher education levels. There was a positive correlation between the COVID-19 knowledge level and HL levels (r=0.108; p<0.001). Conclusion This study reveals the determination of HL and COVID-19 knowledge levels of candidate soldiers and related factors and supports the necessity of proactive participation of individuals with immediate action plans to increase HL and COVID-19 knowledge levels in young adults. Bangladesh Journal of Medical Science Vol. 23 No. 02 April’24 Page: 377-387
