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An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms
Published in K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc, Machine Learning and Deep Learning Techniques for Medical Science, 2022
Y. Khourdifi and M. Bahaj [4] detected BC using digital/digitized to pathology images in the field of medical pathology. Here a network is designed by changing the network parameters and an encoder is used for compressing the data without affecting the original features of images. Images consisting of various wavelengths are also obtained by spectral imaging technique.
Emerging Biomedical Analysis
Published in Lawrence S. Chan, William C. Tang, Engineering-Medicine, 2019
Organic compounds that absorb at the laser wavelength, sublime readily and co-crystallize with the analyte are chosen as the matrix. The most commonly used materials are 2,5-dihydroxybenzoic acid (DHB); 4-hydroxy-3-methoxycinnamic acid (Ferulic acid); 3,5-dimethoxy-4-hydroxycinnamic acid (sinapinic acid) and a-cyano-4-hydroxycinnamic acid (CHC). The MALDI method has been successfully applied to protein, peptide and DNA analyses in biological research for many years. More recently, there has been a rapid increase in the interest in mass spectral imaging directly from biological tissue. Currently, MALDI is the method of choice in MS imaging applications due to its superior physical compatibility with imaging experimentation (Gessel et al. 2014). However, since the sample preparation for MALDI is time consuming and not compatible with commonly used chromatographic separation techniques, it is now generally replaced by ESI for analysis of biomaterials.
Modelling and analysis of skin pigmentation
Published in Ahmad Fadzil Mohamad Hani, Dileep Kumar, Optical Imaging for Biomedical and Clinical Applications, 2017
Ahmad Fadzil Mohamad Hani, Hermawan Nugroho, Norashikin Shamsudin, Suraiya H. Hussein
Spectral imaging filters and camera are used in spectral scanning. There are two types of filter, namely fixed and tuneable filters (Figures 4.23 and 4.24). For fixed filters, the filters are usually placed in the optical path, performing a wavelength selection. A common solution is to incorporate a set of band pass filters into a filter wheel [107]. The recording is performed upon the synchronised rotation of the wheel with the camera acquisition. Spectral scanning is low cost and allows a large selection of filters. However, it is inflexible in terms of filter specifications and even though we have a large selection of filters, the filter wheel has a limited number of slots.
Malignant cell characterization via mathematical analysis of bio impedance and optical properties
Published in Electromagnetic Biology and Medicine, 2021
The diagnostic procedures discussed included the malaise of electrode insertion into the suspected lesion vicinity. An alternate optical procedure such as spectral imaging, cell staining has been opted for cancer cell detection. Soensken et al. developed a spectral cell staining imaging technique for cancer cell diagnosis (Soenksen et al. 1999). The retrieved sample tissue is stained with two separate nature of dyes: one is adherent to normal tissue and the other is adherent to cancerous. Spectrometer is used for spectral analysis of the concentration of the two varied dyes to ascertain the nature of the suspected tissue. Alafano et al. developed an optical technique for analyzing cancer or precancerous tissue with the aid of a fluorescence excitation spectroscopy (Alfano et al. 2000). Initially the suspected lesion tissue is excited with monochromatic light at 268 nm and 289 nm and the ratio of the fluorescence intensities are measured, respectively. If the intensity ratio for 289 to 268 nm light is greater than the threshold magnitude of 1.5 then the suspected tissue is malignant. Alfano et al. supplemented a diagnostic technique for spectral optical imaging using key water absorption wavelength (Alfano et al. 2010). The underlying principle involved water absorption wavelength peaks at 980 nm, 1195 nm, 1456 nm, 1944 nm, 2880 to 3360 nm and 4720 nm for malignant cell. The diagnostic information including spectral optical imaging, polarization imaging and elastic scattering imaging are used for active classification of cancer cells.
Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics
Published in Expert Review of Molecular Diagnostics, 2020
Ihtesham U. Rehman, Rabia Sannam Khan, Shazza Rehman
In literature, significant information is available to clinicians and spectroscopists, including clinical, biological and bio spectral chemical data and outputs of related spectral imaging. A combination of this data provides information that requires precise and accurate evaluation of pathology, chemical changes that may occur due to the progression of the disease process either for early diagnosis or monitoring of the disease process. To bring the entire process from ‘lab to patients’ there is a need to understand the entire process in a rapid and accurate way that can be added to the armory of pathologists and clinicians that will help in avoiding misdiagnosis. To achieve this, employment of artificial intelligence, computer-aided diagnosis; machine learning and artificial neural networks will play a pivotal role in the future. A combination of these methodologies will help in developing learning algorithms capable of analysis and interpretation of clinical data and in integrating them into categorized, defined outputs.
Spectral CT, Low Contrast Dose and Annular Sizing: Spotting the Ghost in the Fog
Published in Structural Heart, 2020
Jonathan R. Weir McCall, Julia Sun
For this reason, efforts to reduce contrast dose whilst maintaining diagnostic quality images are essential. Previous approaches have included reducing the CT tube potential, using high pitch helical acquisition, and even leaving the pig tail catheter in-situ following the invasive coronary angiogram and using this to administer the contrast.9–11 In the current issue of the Journal, Alaiti et al. examine the potential of spectral imaging for reducing contrast doses. Spectral imaging is a developing feature of modern CT scanners. It exploits the different attenuation characteristics that occur at different energy levels of x-rays to characterize tissues in a manner not possible with traditional CT.12 By using the attenuation data from two or more x-ray energy spectrum virtual mono-energetic images can be reconstructed for a broad range of single energies (40–200 keV) each with differing clinical utility. In the case of dose reduction in CT angiograms, low-energy images provide improved definition of contrast material–filled structures and enhance suboptimal contrast studies or angiographic studies with low dose contrast.13