Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models

High-quality images acquired from medical devices can be utilized to aid diagnosis and detection of various diseases. However, such images can be very expensive to acquire and difficult to store, and the process of diagnosis can consume significant time. Automatic diagnosis based on artificial intelligence (AI) techniques can contribute significantly to overcoming the cost and time issues. Pre-trained deep learning models can present an effective solution to medical image classification. In this paper, we propose two such models, ResNext101_ 32×8d and VGG19 to classify two types of brain tumor: pituitary and glioma The proposed models are applied to a dataset consisting of 1,800 MRI images comprising in two classes of diagnoses; glioma tumor and pituitary tumor. A single-image super-resolution (SISR) technique is applied to the MRI images to classify and enhance their basic features, enabling the proposed models to enhance particular aspects of the MRI images and assist the training process of the models. These models are implemented using PyTorch and TensorFlow frameworks with hyper-parameter tuning, and data augmentation. Experimentally, receiver operating characteristic curve (ROCC), the error matrix, Precision, and Recall are used to analyze the performance of the proposed model. Results obtained demonstrate that VGG19 and ResNext101_ 32×8d achieved testing accuracies of 99.98% and 100%, and loss rates of 0.0120 and 0.108, respectively. The F1-score, Precision, Recall, and the area under the ROC for VGG19 were 99.89%, 99.90%, 99.89%, and 100%, respectively, while for the ResNext101_ 32×8d they were all 100%. The proposed models when applied to MRI images to provide a quick and accurate approach to distinguishing between patients with pituitary and glioma tumors, and could aid doctors and radiologists in the screening of patients with brain tumors

Robust and secure fractional wavelet image watermarking

This paper presents an efficient fractional wavelet transform (FWT) image watermarking technique based on combining the discrete wavelet transform (DWT) and the fractional Fourier transform (FRFT). In the proposed technique, the host image is wavelet transformed with two resolution levels, and then, the middle frequency sub-bands are FRFT transformed. The watermark is hidden by altering the selected FRFT coefficients of the middle frequency sub-bands of the 2-level DWT-transformed host image. Two pseudo-random noise (PN) sequences are used to modulate the selected FRFT coefficients with the watermark pixels, and inverse transforms are finally applied to get the watermarked image. In watermark extraction, we just need the same two PN sequences used in the embedding process and the watermark size. The correlation factor is used to determine whether the extracted pixel is one or zero. The proposed fractional wavelet transform (FWT) image watermarking method is tested with different image processing attacks and under composite attacks to verify its robustness. Experimental results demonstrated improved robustness and security

Maximum output voltage and frequency of single electron tunneling inverter for variable temperature and load capacitance

Studying of single electronics should consider both the temperature and the circuit capacitance as being the most important parameters that affect the behavior of SET devices. In this letter, the delay and the output voltage level in the single electron tunneling inverter are investigated for a wide range of temperature and load capacitance. Due to the stochastic nature of the electron transports in single electronics, the results are considered to be the average of many Monte Carlo computations

Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique With ResNext101_32× 8d and VGG19 Pre-Trained Models

High-quality images acquired from medical devices can be utilized to aid diagnosis and detection of various diseases. However, such images can be very expensive to acquire and difficult to store, and the process of diagnosis can consume significant time. Automatic diagnosis based on artificial intelligence (AI) techniques can contribute significantly to overcoming the cost and time issues. Pre-trained deep learning models can present an effective solution to medical image classification. In this paper, we propose two such models, ResNext101_ $32\times 8\text{d}$ and VGG19 to classify two types of brain tumor: pituitary and glioma The proposed models are applied to a dataset consisting of 1,800 MRI images comprising in two classes of diagnoses; glioma tumor and pituitary tumor. A single-image super-resolution (SISR) technique is applied to the MRI images to classify and enhance their basic features, enabling the proposed models to enhance particular aspects of the MRI images and assist the training process of the models. These models are implemented using PyTorch and TensorFlow frameworks with hyper-parameter tuning, and data augmentation. Experimentally, receiver operating characteristic curve (ROCC), the error matrix, Precision, and Recall are used to analyze the performance of the proposed model. Results obtained demonstrate that VGG19 and ResNext101_ $32\times 8\text{d}$ achieved testing accuracies of 99.98% and 100%, and loss rates of 0.0120 and 0.108, respectively. The F1-score, Precision, Recall, and the area under the ROC for VGG19 were 99.89%, 99.90%, 99.89%, and 100%, respectively, while for the ResNext101_ $32\times 8\text{d}$ they were all 100%. The proposed models when applied to MRI images to provide a quick and accurate approach to distinguishing between patients with pituitary and glioma tumors, and could aid doctors and radiologists in the screening of patients with brain tumors