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16 pages, 988 KiB

Open AccessArticle

Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features

by Sinan Alkassar, Mohammed A. M. Abdullah, Bilal A. Jebur, Ghassan H. Abdul-Majeed, Bo Wei and Wai Lok Woo

Appl. Sci. 2021, 11(23), 11461; https://doi.org/10.3390/app112311461 - 3 Dec 2021

Cited by 3 | Viewed by 3579

Abstract

Pneumonia is a severe infection that affects the lungs due to viral or bacterial infections such as the novel COVID-19 virus resulting in mild to critical health conditions. One way to diagnose pneumonia is to screen prospective patient’s lungs using either a Computed [...] Read more.

Pneumonia is a severe infection that affects the lungs due to viral or bacterial infections such as the novel COVID-19 virus resulting in mild to critical health conditions. One way to diagnose pneumonia is to screen prospective patient’s lungs using either a Computed Tomography (CT) scan or chest X-ray. To help radiologists in processing a large amount of data especially during pandemics, and to overcome some limitations in deep learning approaches, this paper introduces a new approach that utilizes a few light-weighted densely connected bottleneck residual block features to extract rich spatial information. Then, shrinking data batches into a single vector using four efficient methods. Next, an adaptive weight setup is proposed utilizing Adaboost ensemble learning which adaptively sets weight for each classifier depending on the scores generated to achieve the highest true positive rates while maintaining low negative rates. The proposed method is evaluated using the Kaggle chest X-ray public dataset and attained an accuracy of 99.6% showing superiority to other deep networks-based pneumonia diagnosis methods. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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21 pages, 31890 KiB

Open AccessArticle

Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples

by Samuel Ortega, Himar Fabelo, Martin Halicek, Rafael Camacho, María de la Luz Plaza, Gustavo M. Callicó and Baowei Fei

Appl. Sci. 2020, 10(13), 4448; https://doi.org/10.3390/app10134448 - 28 Jun 2020

Cited by 12 | Viewed by 2445

Abstract

The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma [...] Read more.

The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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13 pages, 4861 KiB

Open AccessFeature PaperArticle

Fiber Optic Sensor for Real-time Monitoring of Freezing–Thawing Cycle in Cryosurgery

by Dimosthenis Spasopoulos, George Rattas, Archontis Kaisas, Thomas Dalagiannis, Ioannis D. Bassukas, Nikolaos Kourkoumelis and Aris Ikiades

Appl. Sci. 2020, 10(3), 1053; https://doi.org/10.3390/app10031053 - 5 Feb 2020

Cited by 1 | Viewed by 2313

Abstract

Cryosurgery/cryotherapy is a widely used, freezing–thawing technique for the renewal or destruction of pathological tissues by applying localized rapid cooling; however, it still relies on the subjective “expert knowledge” of the physicians without, up to now, real-time monitoring of the treatment. This work [...] Read more.

Cryosurgery/cryotherapy is a widely used, freezing–thawing technique for the renewal or destruction of pathological tissues by applying localized rapid cooling; however, it still relies on the subjective “expert knowledge” of the physicians without, up to now, real-time monitoring of the treatment. This work focused on assessing the depth of freezing using optical transmission and backscattering measurements from frozen/unfrozen porcine ex-vivo skin samples. An optical fiber-array sensor was subsequently developed to determine the depth of freezing and the associated kill zone during freeze–thawing cycles with sub-millimeter accuracy within the skin tissue. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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16 pages, 5131 KiB

Open AccessArticle

Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death

by Andrea Fernández-Pérez, Olga Gutiérrez-Saiz, José Luis Fernández-Luna, Fernando Moreno and José María Saiz

Appl. Sci. 2019, 9(14), 2886; https://doi.org/10.3390/app9142886 - 19 Jul 2019

Cited by 4 | Viewed by 2749

Abstract

Imaging polarimetry is a focus of increasing interest in diagnostic medicine because of its non-destructive nature and its potential to distinguish normal from tumor tissue. However, handling and understanding polarimetric images is not an easy task, and different intermediate steps have been proposed [...] Read more.

Imaging polarimetry is a focus of increasing interest in diagnostic medicine because of its non-destructive nature and its potential to distinguish normal from tumor tissue. However, handling and understanding polarimetric images is not an easy task, and different intermediate steps have been proposed in order to introduce helpful physical magnitudes. In this research, we look for a sensitive polarimetric parameter that allows us to detect cell death when cancer cells are treated with chemotherapy drugs. Experiments in two different myelomonocytic leukemia cell lines, U937 and THP1, are performed in triplicate, finding a highly-significant positive correlation between total diattenuation of samples in transmission configuration,

D_{T}

, and chemotherapy-induced cell death. The location of the diattenuation enhancement gives some insight into the cell death process. The proposed method can be an objective complement to conventional methodologies based on pure observational microscopy and can be easily implemented in regular microscopes. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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23 pages, 2172 KiB

Open AccessArticle

Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology

by Chen Qiao, Lujia Lu, Lan Yang and Paul J. Kennedy

Appl. Sci. 2019, 9(10), 2148; https://doi.org/10.3390/app9102148 - 26 May 2019

Cited by 8 | Viewed by 4053

Abstract

Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial [...] Read more.

Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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13 pages, 3875 KiB

Open AccessArticle

Non-Invasive Morphological Characterization of Rice Leaf Bulliform and Aerenchyma Cellular Regions Using Low Coherence Interferometry

by Hyeree Kim, XiaoXuan Du, Sungwook Kim, Pilun Kim, Ruchire Eranga Wijesinghe, Byoung-Ju Yun, Kyung-Min Kim, Mansik Jeon and Jeehyun Kim

Appl. Sci. 2019, 9(10), 2104; https://doi.org/10.3390/app9102104 - 22 May 2019

Cited by 7 | Viewed by 3831

Abstract

Non-invasive investigation of rice leaf specimens to characterize the morphological formation and particular structural information that is beneficial for agricultural perspective was demonstrated using a low coherence interferometric method called swept source optical coherence tomography (SS-OCT). The acquired results non-invasively revealed morphological properties [...] Read more.

Non-invasive investigation of rice leaf specimens to characterize the morphological formation and particular structural information that is beneficial for agricultural perspective was demonstrated using a low coherence interferometric method called swept source optical coherence tomography (SS-OCT). The acquired results non-invasively revealed morphological properties of rice leaf, such as bulliform cells; aerenchyma, parenchyma, and collenchyma layer; and vascular bundle. Beside aforementioned morphologic characteristics, several leaf characteristics associated with cytological mechanisms of leaf rolling (leaf inclination) were examined for the pre-identification of inevitable necrosis and atrophy of leaf tissues by evaluating acute angle information, such as angular characteristics of the external bi-directional angles between the lower epidermis layer and lower mid-vein, and internal angle of lower mid-vein. To further assist the pre-identification, acquired cross-sections were employed to enumerate the small veins of each leaf specimen. Since mutants enlarge leaf angles due to increased cell division in the adaxial epidermis, healthy and abnormal leaf specimens were morphologically and quantitatively compared. Therefore, the results of the method can be used in agriculture, and SS-OCT shows potential as a rigorous investigation method for selecting mutant infected rice leaf specimens rapidly and non-destructively compared to destructive and time consuming gold-standard methods with a lack of precision. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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Review

Jump to: Research

11 pages, 1632 KiB

Open AccessReview

Emerging Optical Techniques for the Diagnosis of Onychomycosis

by Chrysoula Petrokilidou, Georgios Gaitanis, Ioannis D Bassukas, Aristea Velegraki, Edgar Guevara, Martha Z Vardaki and Nikolaos Kourkoumelis

Appl. Sci. 2020, 10(7), 2340; https://doi.org/10.3390/app10072340 - 29 Mar 2020

Cited by 7 | Viewed by 3087

Abstract

Onychomycosis is the most prevalent nail infection. Although it is not a life-threatening condition, it impacts the quality of life for many patients and often imposes a challenging diagnostic problem. The causative agents are dermatophytes, yeasts and non-dermatophytic moulds. Accurate and early diagnosis, [...] Read more.

Onychomycosis is the most prevalent nail infection. Although it is not a life-threatening condition, it impacts the quality of life for many patients and often imposes a challenging diagnostic problem. The causative agents are dermatophytes, yeasts and non-dermatophytic moulds. Accurate and early diagnosis, including the identification of the causative species, is the key factor for rational therapy. Still, early diagnosis is not optimal as the current gold standard for the differentiation of the infectious agents is culture-based approaches. On the other hand, noninvasive optical technologies may enable differential diagnosis of nail pathologies including onychomycosis. When light penetrates and propagates along the nail tissue, it interacts in different ways with the components of either infected or healthy nail segments, providing a wealth of diagnostic information upon escaping the tissue. This review aims to assess alternative optical techniques for the rapid diagnosis of onychomycosis with a potential to monitor therapeutic response or even identify the fungal agent non-invasively and in real time in a clinical setting. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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30 pages, 2056 KiB

Open AccessFeature PaperReview

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

by A. Prabhakara Rao, Neeraj Bokde and Saugata Sinha

Appl. Sci. 2020, 10(3), 767; https://doi.org/10.3390/app10030767 - 21 Jan 2020

Cited by 33 | Viewed by 6736

Abstract

In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review [...] Read more.

In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review attempts to present the clinical evidence in support of using photoacoustic imaging for breast cancer detection. Based on the experimental evidence obtained from the clinical studies conducted so far, the performance of photoacoustic imaging is compared with that of conventional breast imaging modalities. While we find that there is enough experimental evidence to support the use of photoacoustic imaging for breast cancer detection, additional clinical studies are required to be performed to evaluate the diagnostic potential of photoacoustic imaging for identifying different types of breast cancer. To establish the utility of photoacoustic imaging for breast cancer screening, clinical studies with high-risk asymptomatic patients need to be done. Full article

(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)

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