Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or progno...
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Under the impact of global climate changes and human activities, harmful algae blooms (HABs) in surface waters have become a growing concern due to negative im-pacts on water related industries, such as tourism, fishi...
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Under the impact of global climate changes and human activities, harmful algae blooms (HABs) in surface waters have become a growing concern due to negative im-pacts on water related industries, such as tourism, fishing and safe water supply. Many jurisdictions have introduced specific water quality regulations to protect public health and safety. Currently, drinking water quality guidelines related to cyanobacteria are based on maximum acceptable concentrations of toxins or elevated levels of cyanobacte-ria cells in water supplies. Therefore, reliable and cost effective methods of quantifying the type and concentration of threshold levels of algae cells has become critical for ensuring successful water management. In this work, we present SAMSON, an in-novative system to automatically classify multiple types of algae from different phyla groups by combining standard morphological features with their multi-wavelength sig-nals. Two phyla with focused investigation in this study are the Cyanophyta phylum (blue-green algae), and the Chlorophyta phylum (green algae). To accomplish this, we use a custom-designed microscopy imaging system which is configured to image water samples at two uorescent wavelengths and seven absorption wavelengths using discrete-wavelength high-powered light emitting diodes (LEDs). Powered by computer vision and machine learning, we investigate the possibility and effectiveness of auto-matic classification using a deep residual convolutional neural network. More specifically, a classification accuracy of 96% was achieved in an experiment conducted with six different algae types. This high level of accuracy was achieved using a deep resid-ual convolutional neural network that learns the optimal combination of spectral and morphological features. These findings elude to the possibility of leveraging a unique fingerprint of algae cell (i.e. spectral wavelengths and morphological features) to auto-matically distinguish different algae types. Our wo
Medical image analysis has become a topic under the spotlight in recent years. There is a significant progress in medical image research concerning the usage of machine learning. However, there are still numerous ques...
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This paper presents SAMSON, a Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae within a water sample. designed to be portable and low-cost for on-site use, the optical sub-system of SAMS...
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Pattern recognition (PR) based myoelectric control could provide intuitive and dexterous control of advanced prostheses. Previous studies showed that the performance of finger movements was not as good as that of wris...
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Genetic algorithms (GAs) have a long history of over four decades. GAs are adaptive heuristic search algorithms that provide solutions for optimization and search problems. The GA derives expression from the biologica...
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We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum switch based on all optical routing of single photon by single photon, a quantum-dot spin in a double-sided optical microcavity with two ph...
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Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change...
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Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. Th...
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Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. This paper presents, examines and compares two different approaches to skin lesion segmentation. The first approach uses U-Nets and introduces a histogram equalization based preprocessing step. The second approach is a C-Means clustering based approach that is much simpler to implement and faster to execute. The Jaccard Index between the algorithm output and hand segmented images by dermatologists is used to evaluate the proposed algorithms. While many recently proposed deep neural networks to segment skin lesions require a significant amount of computational power for training (i.e., computer with GPUs), the main objective of this paper is to present methods that can be used with only a CPU. This severely limits, for example, the number of training instances that can be presented to the U-Net. Comparing the two proposed algorithms, U-Nets achieved a significantly higher Jaccard Index compared to the clustering approach. Moreover, using the histogram equalization for preprocessing step significantly improved the U-Net segmentation results.
Many automatic skin lesion diagnosis systems use segmentation as a preprocessing step to diagnose skin conditions because skin lesion shape, border irregularity, and size can influence the likelihood of malignancy. Th...
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