Tuberculosis remains a significant health challenge worldwide, affecting a large population. Therefore, accurate diagnosis of this disease is a critical issue. With advancements in computer systems, imaging devices, and rapid progress in machine learning, tuberculosis diagnosis is being increasingly performed through image analysis. This study proposes three segmentation models based on U-Net, V-Net, and Seg-Net architectures to improve tuberculosis detection using the Shenzhen and Montgomery databases. These deep learning-based methods aim to enhance segmentation accuracy by employing advanced preprocessing techniques, attention mechanisms, and non-local blocks. Experimental results indicate that the proposed models outperform traditional approaches, particularly in terms of the Dice coefficient and accuracy values. The models have demonstrated robust performance on popular datasets. As a result, they contribute to more precise and reliable lung region segmentation, which is crucial for the accurate diagnosis of respiratory diseases like tuberculosis. In evaluations using various performance metrics, the proposed U-Net and V-Net models achieved Dice coefficient scores of 96.43% and 96.42%, respectively, proving their competitiveness and effectiveness in medical image analysis. These findings demonstrate that the Dice coefficient values of the proposed U-Net and V-Net models are more effective in tuberculosis segmentation than Seg-Net and other traditional methods.

This study presents a comprehensive comparative analysis of machine learning, deep learning, and optimization-based hybrid methods for malicious URL detection on the Malicious Phish dataset. For feature selection and model hyperparameter tuning, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harris Hawk Optimizer (HHO) were employed. Both multiclass and binary classification tasks were addressed using classic machine learning algorithms such as LightGBM, XGBoost, and Random Forest, as well as deep learning models including LSTM, CNN, and hybrid CNN+LSTM architectures, with optimization support also integrated into these models. The experimental results reveal that the ELECTRA-based deep learning model achieved outstanding accuracy and F1-scores of up to 99% in both multiclass and binary scenarios. Although optimization-supported hybrid models also improved performance, the language-model-based ELECTRA architecture demonstrated a significant superiority over classical and optimized approaches. The findings indicate that optimization algorithms are effective in feature selection and enhancing model performance, yet next-generation language models clearly set a new benchmark in malicious URL detection.

Image interpolation is an important topic in the field of image processing. It is defined as the process of transforming low-resolution images into high-resolution ones using image processing methods. Recent studies on interpolation have shown that researchers are focusing on successful interpolation techniques that preserve edge information. Therefore, the edge detection phase plays a vital role in interpolation studies. However, these approaches typically rely on gradient-based linear computations for edge detection. On the other hand, non-linear structures that effectively simulate the human visual system have gained attention. In this study, a non-linear method was developed to detect edge information using a pixel similarity approach. Pixel similarity-based edge detection approach offers both lower computational complexity and more successful interpolation results compared to gradient-based approaches. 1D cubic interpolation was applied to the pixels identified as edges based on pixel similarity, while bicubic interpolation was applied to the remaining pixels. The algorithm was tested on 12 commonly used images and compared with various interpolation techniques. The results were evaluated using metrics such as SSIM and PSNR, as well as visual assessment. The experimental findings clearly demonstrated that the proposed method outperformed other approaches. Additionally, the method offers significant advantages, such as not requiring any parameters and having competitive computational cost.

Image watermarking serves as a crucial technique for protecting copyrights and verifying the ownership of digital images. The watermarking process involves embedding data into images to prevent unauthorized usage of digital content. In this study, an innovative image watermarking method, using Spiking Neural Networks to embed robust and imperceptible watermarks, is proposed. The proposed method benefits from the edge detection capabilities of spiking neural networks to identify optimal regions for watermark locations. By targeting edge detection, this watermarking approach ensures significant resistance to common image processing attacks such as compression, noise addition, and cropping, while maintaining minimal perceptual distortion. The edge image obtained with the spiking neural network approach is divided into 16 × 16 non-overlapping blocks, and edge pixel definitions are made. Moreover, to increase the security level, the watermark image is scrambled with the help of a chaotic substitution box. The scrambled image is placed on the pixels marked as edges in the edge image. Afterward, it is divided into sub-bands by applying a discrete wavelet transform. The watermark image is inserted into the HH (High-High) band with the help of the discrete wavelet transform and the singular value decomposition approach. In the extraction stage, the HH band of the original image is used together with the watermarked image. Comprehensive experiments are conducted to evaluate the proposed technique, revealing its superiority in preserving both image quality and watermark integrity compared to conventional approaches.

Image histograms are frequently used as a feature vector in content-based image retrieval (CBIR). The related methodology involves processing of a single channel histogram on gray leve ...

Histogram sayısal görüntülerdeki piksellerin dağılımını gösteren önemli bir bilgi temsil yöntemidir. Gri seviyeli görüntülerde tek boyutlu dizi işlenirken, renkli görüntülerde üç boyutlu dizinin analizinin yapılması gereklidir. Dolayısıyla renkli görüntülerde histogram işleminin hesapsal maliyeti yüksektir. Her kanaldan alınan tek boyutlu histogram bilgisinin birleştirilmesi ise ayrıca bir problemdir. Bu çalışmada renkli görüntülerde Kırmızı-Yeşil-Mavi (KYM) renk uzayı kullanılarak tek boyutlu histogram üreten yeni bir teknik geliştirilmiştir. Önerilen yaklaşımda, öncelikle her kanal için Otsu ve Kapur eşikleme yöntemleri kullanılarak eşikler elde edilmiş, akabinde renk uzayı söz konusu eşikler yardımıyla 8 adet prizmaya bölünmüştür. Oluşturulan prizma içerisinde kalan pikseller aynı sınıfa atanarak kümeleme yapılmıştır. İlave olarak aynı sınıfa dâhil olan piksellerin ortalama değeri kullanılarak renk indirgemesi yapılmıştır. Böylece elde edilen görüntülerdeki bilgi kaybı tepe sinyal gürültü oranı (Peak Signal Noise Ratio: PSNR) ölçütü ile değerlendirilmiştir.