Details Of Medical Images Research Articles

An Computational Framework for Enhanced Early Detection of Diabetic Retinopathy with Transfer Learning Methods

Easier early spotting of diabetes retinopathy is important for getting patients help quickly and improving their results. In this research, we used transfer learning techniques to make deep learning models better at automatically finding diabetic retinopathy. Firstly, we used methods for "data augmentation" to make our training sample more diverse. This made our models better at generalization. After this proposed method used fine-tuned the hyperparameters of the transfer learning models. Four transfer learning models caught our attention as MobileNetV2, EfficientNet, VGG16, and a Hybrid Ensemble (DenseNet + Inception). The method focused on the DenseNet model because it had good results in past research and could clearly pick out complex details in medical Images. When combined with data enhancement and fine-tuning, our experiments showed that the DenseNet model worked better than the others in terms of both accuracy and speed. This model, DenseNet, had an amazing success rate of 88.81%, showing that it could be a useful tool for finding diabetic retinopathy early on. The work shows how important transfer learning is in medical image analysis and how to use deep learning models most effectively for finding diabetic retinopathy early. Using advanced machine learning methods to help find and fix diabetic retinopathy will be studied more in the future.

Medical Image Segmentation for Brain Tumor Detection Using U-NET

Abstract: Brain tumors encompass a wide variety of growths that can occur in the cranial cavity. These tumors can be benign or malignant, and early identification is important for suitable treatment. MRI is a non-invasive imaging method that provides detailed anatomical information and is useful in diagnosing and monitoring brain tumors. However, identifying tumors from complex MRI data remains a difficult and challenging task. This project uses image segmentation techniques to identify and identify tumors on MRI scans. The technology is works on the principles of deep learning, an intelligence process that enables computers to learn and recognize patterns in large data sets. This learning process helps doctors make informed decisions by distinguishing between healthy brain tissue and tumor growth. The unique design of the U-Net architecture, characterized by a U-shaped pattern with compactness and detail, makes it possible to capture complex patterns and fine details in medical images. Using different data from many brain MRI scans, the U-Net algorithm was trained to depict tumor regions with pixel-level accuracy.

ColorMedGAN: A Semantic Colorization Framework for Medical Images

Colorization for medical images helps make medical visualizations more engaging, provides better visualization in 3D reconstruction, acts as an image enhancement technique for tasks such as segmentation, and makes it easier for non-specialists to perceive tissue changes and texture details in medical images in diagnosis and teaching. However, colorization algorithms have been hindered by limited semantic understanding. In addition, current colorization methods still rely on paired data, which is often not available for specific fields such as medical imaging. To address the texture detail of medical images and the scarcity of paired data, we propose a self-supervised colorization framework based on CycleGAN(Cycle-Consistent Generative Adversarial Networks), treating the colorization problem of medical images as a cross-modal domain transfer problem in color space. The proposed framework focuses on global edge features and semantic information by introducing edge-aware detectors, multi-modal discriminators, and a semantic feature fusion module. Experimental results demonstrate that our method can generate high-quality color medical images.

PyDiNet: Pyramid Dilated Network for medical image segmentation

Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architectures: fully convolutional networks and U-Net-based architecture. These methods rely on multiple pooling and striding layers leading to the loss of important spatial information and fail to capture details in medical images. In this paper, we propose a novel neural network called PyDiNet (Pyramid Dilated Network) to capture small and complex variations in medical images while preserving spatial information. To achieve this goal, PyDiNet uses a newly proposed pyramid dilated module (PDM), which consists of multiple dilated convolutions stacked in parallel. We combine several PDM modules to form the final PyDiNet architecture. We applied the proposed PyDiNet to different medical image segmentation tasks. Experimental results show that the proposed model achieves new state-of-the-art performance on three medical image segmentation benchmarks. Furthermore, PyDiNet was very competitive on the 2020 Endoscopic Artifact Detection challenge.

Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution

The development of convolutional neural networks has promoted the progress of computer-aided diagnostic systems. Details in medical image, such as the texture and tissue structure, are crucial features for diagnosis. Therefore, large input images combined with deep convolution neural networks are adopted to boost the performance in recent research of chest X-ray diagnosis. Meanwhile, due to the variable sizes of thoracic diseases, many researchers have worked to introduce additional module to capture multi-scale feature of images in CNN. However, these efforts hardly consider the computational costs of large inputs and introduced additional modules. This paper aims to automatically diagnose diseases on chest X-rays images quickly and effectively. We propose the multi-kernel depthwise convolution(MD-Conv) which contains depthwise convolution kernels with different filter sizes in one depthwise convolution layer. MD-Conv has high calculation efficiency and few parameters. Because its ability to learn multi-scale feature based on the multi-size kernels, it is appropriate for medical images diagnosis tasks in which abnormalities varied in sizes. In addition, larger depthwise convolution kernels are adopted in MD-Conv to obtain a larger receptive field efficiently, which can ensure sufficient receptive field for high resolution inputs. MD-Conv can be easily applied in modern lightweight networks to replace the normal depthwise convolution layer. We conduct experiments on the Chest X-ray 14 Dataset, which is the largest available chest x-ray dataset, and obtain competitive results. We also evaluate the MD-Conv on the new released dataset for pediatric pneumonia diagnosis. We obtain a better performance of 98.3% AUC than original paper (96.8%) for recognize pneumonia versus normal. Meanwhile we compare the FLOPs and Params of different models to show their efficiency for chest X-rays recognition.

Using the Sonification for Hardly Detectable Details in Medical Images

The inverse sonification problem is investigated in this article in order to detect hardly capturing details in a medical image. The direct problem consists in converting the image data into sound signals by a transformation which involves three steps - data, acoustics parameters and sound representations. The inverse problem is reversing back the sound signals into image data. By using the known sonification operator, the inverse approach does not bring any gain in the sonified medical imaging. The replication of the image already known does not help the diagnosis and surgical operation. In order to bring gains in the medical imaging, a new sonification operator is advanced in this paper, by using the Burgers equation of sound propagation. The sonified medical imaging is useful in interpreting the medical imaging that, however powerful they may be, are never good enough to aid tumour surgery. The inverse approach is exercised on several medical images used to surgical operations.

A New Method for Medical Image Denoising using DTCWT and Bilateral Filter

The quality of digital medical images plays vital role in Non-invasive imaging techniques, which are suitable for medical diagnosis and treatment. Removal of noise from a noisy image without losing the diagnostic details in medical image is still a challenging task even though several denoising methods have been proposed since past years. The wavelet thresholding ap-proach has been reported to be a highly successful method for image denoising. However, the main problem experienced in wavelet thresholding is smoothening of edges. In order to retain original texture while denoising medical images, several methods have been reported in literature. In this paper, we proposed, a new method based on combination of dual-tree complex wavelet transform (DTCWT) and bilateral filters for denoising of medical images. The proposed models are experimented on standard medical images, like MRI image of knee contaminated with Rician noise, CT Scan image of brain contaminated with Gaussian noise, Ultrasound image of liver contaminated with Speckle noise. The results have shown that denoised images using the proposed ap-proach have better performance in terms of smoothness and ac-curacy compared with existing methods. To assess quality of denoised images the quality metrics, the standard Signal to Noise Ratio (SNR), Universal Image Quality Index (UQI) Mean square error (MSR), and Structural Similarity Index (SSIM) are em-ployed.

Cloud-based Reversible Dynamic Secure Steganography Model for embedding pathological report in medical images

ABSTRACT Over past decades, several research works have been published in the direction of hiding confidential data that includes only the patients personal details in medical images. The medical images and respective pathological reports are stored separately in the storage device. Storing medical images and pathological reports separately occupy more storage compared to storing pathological reports Stego in medical images. This research identified pixel space available in the medical image for storing other relevant secret information. In this technique, a new algorithm is proposed for generating secret keys. The secret keys are utilized to segment the medical image into five regions: (i) one Region of Interest (ROI) sub-image and (ii) four non-Region of Interest sub-images. From the four non-Region of Interest sub-images, a particular non-ROI region sub-image is randomly selected from the secret key matrix. The pathological report is embedded into the preprocessed selected non-ROI region sub-image of the medical image and generated the Stego medical image. The Stego medical image and secret key are communicated using various network communication applications to the user for deStego the original X-ray image and pathological report at the user end. The research work confirmed the quality of the medical image using MSE and PSNR values.

Using the Sonification for Hardly Detectable Details in Medical Images

Using the Sonification for Hardly Detectable Details in Medical Images

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures. Keywords: Ripplet, Directional discrete cosine transform (DDCT), Peak signal to noise ratio, MSE (mean square error), SC (structural content), MD (maximum difference), NCC (normalized cross correlation

Self-Adapted Layer Contrast Enhancement Algorithm Based on Medical Image

the paper suggests a kind of self-adapted layer contrast enhancement algorithm for medical images, which, in reference to Laplacian pyramid function, could compose images, and enhance pixel contrast on each layer through F/E processing and use of self-adaptation Sigmoid function, and it uses layer enhancement factor to control the enhancement degree. The result shows that details of medical images can be displayed clearly with this algorithm without selecting characteristic center dimensions. It will not generate artifacts and thus improves visual effect of medical images.