Medical Image Enhancement by a Bilateral Filter Using Optimization Technique

Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images

PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progression and providing patients access to timely therapies. However, accurately detecting PD in its early stages remains challenging. This study aims to develop an optimized deep learning model for PD classification using voice signals and hand-drawn spiral images, leveraging a ZFNet-LHO-DRN. The proposed model first preprocesses the input voice signal using a Gaussian filter to remove noise. Features are then extracted from the preprocessed signal and passed to ZFNet to generate output-1. For the hand-drawn spiral image, preprocessing is performed with a bilateral filter, followed by image augmentation. Here also, the features are extracted and forwarded to DRN to form output-2. Both classifiers are trained using the LHO algorithm. Finally, from the output-1 and output-2, the best one is selected based on the majority voting. The ZFNet-LHO-DRN model demonstrated excellent performance by achieving a premium accuracy of 89.8%, a NPV of 89.7%, a PPV of 89.7%, a TNR of 89.3%, and a TPR of 90.1%. The model's high accuracy and performance indicate its potential as a valuable tool for assisting in the early diagnosis of PD.

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Ultra low-parameter denoising: Trainable bilateral filter layers in computed tomography

BACKGROUND

Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms.

PURPOSE

Most data-driven denoising techniques are based on deep neural networks and, therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity.

METHODS

This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design.

RESULTS

Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures (SSIM) of 0.7094 and 0.9674 and peak signal-to-noise ratio (PSNR) values of 33.17 and 43.07 on the respective data sets.

CONCLUSIONS

Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures. This article is protected by copyright. All rights reserved.

Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement

Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively.