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1. Novel Keratoconus Detection Method Using Smartphone

   https://doi.org/10.1109/HI-POCT45284.2019.8962648  

   Askarian, Behnam; Tabei, Fatemehsadat; Tipton, Grace Anne; Chong, Jo Woon (November 2019, Novel Keratoconus Detection Method Using Smartphone)

   Keratoconus is a progressive corneal disease which may cause blindness if it is not detected in the early stage. In this paper, we propose a portable, low-cost, and robust keratoconus detection method which is based on smartphone camera images. A gadget has been designed and manufactured using 3-D printing to supplement keratoconus detection. A smartphone camera with the gadget provides more accurate and robust keratoconus detection performance. We adopted the Prewitt operator for edge detection and the support vector machine (SVM) to classify keratoconus eyes from healthy eyes. Experimental results show that the proposed method can detect mild, moderate, advanced, and severe stages of keratoconus with 89% accuracy on average. 

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2. Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

   https://doi.org/10.3390/sym11060790  

   Kalwa, Upender; Legner, Christopher; Kong, Taejoon; Pandey, Santosh (June 2019, Symmetry)

   Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems. 

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3. Classification of Aortic Stenosis Using Time-Frequency Features from Chest Cardio-mechanical Signals

   https://doi.org/10.1109/TBME.2019.2942741  

   Yang, Chenxi; Aranoff, Nicole; Green, Philip; Tavassolian, Negar (September 2019, IEEE Transactions on Biomedical Engineering)

   Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. Methods: Experimental measurements were collected from twenty patients with AS and twenty healthy subjects. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. Results: In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. Conclusion: The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz. Significance: The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations. 

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4. Late Deep Fusion of Color Spaces to Enhance Finger Photo Presentation Attack Detection in Smartphones

   https://doi.org/10.3390/app122211409  

   Marasco, Emanuela; Vurity, Anudeep (November 2022, Applied Sciences)

   Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art. 

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5. Flow‐ and Fracture‐Driven Bubble Throat Growth Rates and Dynamic Permeability in Crystallizing Magma

   https://doi.org/10.1029/2024GC011932  

   Moitra, P.; Albadi, A.; Tatsch, A.; Santra, P. (January 2025, Geochemistry, Geophysics, Geosystems)

   Abstract Pyroclasts typically exhibit coalesced vesicle textures, which are the evidence of bubble coalescence and the incomplete bubble wall retraction in magma during volcanic eruptions. The sizes of bubble throats or inter‐bubble apertures in permeable networks control the extent of magma outgassing, and therefore, quantifying the growth rates of the bubble throats is important but has remained poorly constrained. Using dynamically similar experiments with spontaneous bursting of a single bubble in rheologically well‐characterized particulate suspensions, we investigate the growth rate of bubble throats for a range of particle volume fractions. For suspensions with 0.50 particle volume fraction, a circular hole (bubble throat) forms following bubble bursting, which after an initial fast growth starts plateauing at a throat‐bubble size ratio of 0.20. The throat growth time scale overall increases with increasing particle volume fraction due to the increase in suspension viscosity. On the other hand, bubbles in suspensions with particle volume fraction near the maximum packing fraction (0.64) exhibit a fracture‐like opening. Thus, our experimental results suggest that the plateauing of the bubble throat growth in crystal‐poor to crystal‐rich magma likely contributes to the wide occurrence of the incompletely retracted vesicle walls in pyroclasts. The implications of the flow‐ to fracture‐like growth of bubble throats on the development of dynamic permeability in magma are discussed. 

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