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- MakaleAçık Erişim2025Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods
Reis, Hatice Catal; Turk, Veysel
Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.
- MakaleAçık Erişim2024Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection
Catal Reis, Hatice; Turk, Veysel
Early detection of plant diseases is critical to prevent disease spread and assist farmers. Thanks to their high discrimination ability, Convolutional Neural Network (CNN)-based architectures can offer practical solutions in identifying different plant diseases. This study proposes a combined method to classify wheat plant diseases using the Integrated Deep Learning Framework (IDLF) and ensemble learning (EL) model. The proposed method uses pre-trained deep neural networks (ImageNet -based model (w/pre-training)). In addition, in the classification process, the performance of hybrid methods consisting of 13 deep learning architectures (DLA) trained from scratch, pre-trained DLA, deep, and-machine learning models were analyzed. Moreover, the use of hypercolumn, contrast stretching, and “Contrast Limited Adaptive Histogram Equalization (CLAHE)” techniques are used to improve the image quality of the dataset. It is aimed to obtain a strong classification performance by combining these methods. In the experimental process, in the study using the original dataset, the RegNetY080 model trained from scratch had an accuracy value of 97.64%; in the study with the optimized dataset with CLAHE, an accuracy value of 98.33%; in the study performed with the CLAHE-improved dataset and transfer learning method, an accuracy value of 99.58% was obtained. An accuracy of 99.58% was achieved in the proposed hybrid study with the pre-trained RegNetY080 model and Random Forest (RF). The proposed method using IDLF and EL model provided 99.72% accuracy. When the proposed method was applied to the Wheat Leaf Dataset and Wheat Leaf Disease dataset, 97.56% accuracy was obtained for the Wheat Leaf Dataset and 99.43% for Wheat Leaf Disease. The results of this study can be helpful to experts and farmers in the detection of plant diseases.
- MakaleYayıncıda açık erişim2024Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks
Catal Reis, Hatice; Turk, Veysel
Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.