Precise diagnosis of lung cancer enabled by improved FTIR-based machine learning

Image super-resolution techniques overcome the limitations of physical limits on infrared imaging systems and allow higher-resolution images of targets to be obtained on top of existing systems. In this paper, we proved that infrared images are more reconstructed than visible images based on data compression. We propose a CNN-based Gradient Residual Attention Network (GRAN) for infrared image super-resolution. Specifically, the residual dense module (RDB) is used to acquire depth features and the gradient operator (GO) gains fine-grained detail features. Meanwhile, the 3D attention block (3DAB) learns features' channel and spatial correlation to selectively capture more useful informative features. The experimental results show that the proposed method performs well compared to popular image super-resolution methods in terms of quantitative metrics and visual quality. 

Gradient residual attention network for infrared image super-resolution

Globally, cancer is regarded as a severe health issue. Among these is breast cancer (BC), the second most common kind in women after skin cancer. According to statistical data from the NIH Cancer Institute, the expected number of cases was around 3 lakh people, with a 7.1 % fatality rate. The rise of multidrug resistance (MDR) in British Columbia is a severe issue as it lowers the effectiveness of existing treatments. Traditional MDR treatment approaches in BC have a number of drawbacks, including toxicity, high cost, early discharge, restricted drug load before target, and poor effectiveness. Preventing MDR BC has depended heavily on developing innovative therapy delivery methods utilizing nanomedicine and new approaches to diagnosing and treating BC. This review discusses MDR in BC and its pathophysiology in relation to tumor kinetics. It focuses on the latest therapeutic approaches over the traditional methods like Surgery, Chemotherapy, Nanomedicine, Phototherapy, Immunotherapy, and Gene therapy. It also describes diagnostic techniques using MRI/PET, Mammography, Microwave Sensing, In-silico methods, Molecular diagnostic techniques, and AI-machine learning-based tools. It also discussed the limitations of nanoparticles in diagnosing and treating BC. This review article aims to identify new and efficient ways to diagnose and treat MDR BC and prevent tumor recurrence. 

Implications of nanotherapeutic advancements to leverage multi-drug resistant breast cancer: The state-of-the-art review

Infrared microspectroscopy beamline BL06B at SSRF

This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched-filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one-dimensional quantum integer wavelet S-transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD-PCSANN-DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD-PCSANN-DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD-CNN), breast cancer classification from thermal images utilizing Grunwald-Letnikov assisted dragonfly algorithm-based deep feature selection (BCD-VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD-SqueezeNet), respectively. RESEARCH HIGHLIGHTS: The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD-PCSANN-DBO method is implemented using Python.

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

Micro-tomographic and infrared spectral data mining for breast cancer diagnosis

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Micro-tomographic and infrared spectral data mining for breast cancer diagnosis