scholarly journals StoolNet for Color Classification of Stool Medical Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1464 ◽  
Author(s):  
Ziyuan Yang ◽  
Lu Leng ◽  
Byung-Gyu Kim

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.


2020 ◽  
pp. 1557-1579
Author(s):  
Nassima Bouadem ◽  
Rahim Kacimi ◽  
Abdelkamel Tari

Wireless Sensor Networks (WSNs) became omnipresent in our daily life. As a result, they have emerged as a fruitful research topic, because of their advantages, especially their low cost and easy deployment. However, these attractive merits imply that available resources, especially energy, in each sensor node have to be wisely used through different network dynamics. Beside other techniques, duty-cycling (DC) is the first widely used one to save energy in WSNs. However, due to the continuous changes, mainly in the energy availability, the nodes have to operate in a very low DC which is a required strategy in many applications in order to keep the network operational. This article presents a detailed survey that provides an interesting view of different DC schemes which are proposed to tackle the specific WSN challenges, and it also gives a novel classification of DC schemes that includes the most recent techniques. The last part aims to investigate the impact of the low DC on both the network and the application layer.


2010 ◽  
Author(s):  
Thavavel V ◽  
JafferBasha J

Segmentation forms the onset for image analysis especially for medical images, making any abnormalities in tissues distinctly visible. Possible application includes the detection of tumor boundary in SPECT, MRI or electron MRI (EMRI). Nevertheless, tumors being heterogeneous pose a great problem when automatic segmentation is attempted to accurately detect the region of interest (ROI). Consequently, it is a challenging task to design an automatic segmentation algorithm without the incorporation of ‘a priori’ knowledge of an organ being imaged. To meet this challenge, here we propose an intelligence-based approach integrating evolutionary k-means algorithm within multi-resolution framework for feature segmentation with higher accuracy and lower user interaction cost. The approach provides several advantages. First, spherical coordinate transform (SCT) is applied on original RGB data for the identification of variegated coloring as well as for significant computational overhead reduction. Second the translation invariant property of the discrete wavelet frames (DWF) is exploited to define the features, color and texture using chromaticity of LL band and luminance of LH and HL band respectively. Finally, the genetic algorithm based K-means (GKA), which has the ability to learn intelligently the distribution of different tissue types without any prior knowledge, is adopted to cluster the feature space with optimized cluster centers. Experimental results of proposed algorithm using multi-modality images such as MRI, SPECT, and EMRI are presented and analyzed in terms of error measures to verify its effectiveness and feasibility for medical applications.


2022 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Inês Vigo ◽  
Luis Coelho ◽  
Sara Reis

Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer’s Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.


Author(s):  
Nassima Bouadem ◽  
Rahim Kacimi ◽  
Abdelkamel Tari

Wireless Sensor Networks (WSNs) became omnipresent in our daily life. As a result, they have emerged as a fruitful research topic, because of their advantages, especially their low cost and easy deployment. However, these attractive merits imply that available resources, especially energy, in each sensor node have to be wisely used through different network dynamics. Beside other techniques, duty-cycling (DC) is the first widely used one to save energy in WSNs. However, due to the continuous changes, mainly in the energy availability, the nodes have to operate in a very low DC which is a required strategy in many applications in order to keep the network operational. This article presents a detailed survey that provides an interesting view of different DC schemes which are proposed to tackle the specific WSN challenges, and it also gives a novel classification of DC schemes that includes the most recent techniques. The last part aims to investigate the impact of the low DC on both the network and the application layer.


Author(s):  
J.C. Felipe ◽  
J.B. Olioti ◽  
A.J.M. Traina ◽  
M.X. Ribeiro ◽  
E.P.M. Sousa ◽  
...  

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 30-37
Author(s):  
Mamatha Kp ◽  
H.N. Suma

The proposed work is to present an effective approach to diagnoseof dementia using MRI images and classify into different stages. There are many manual segmentation algorithms on detection and classification or very simple and specific segmentation algorithms to segment each region of interest exclusively. Thus, the proposed system shall use one of the most effective automatic segmentation techniques on MRI images at once. The regions of interest to segment are CSF (Cerebralspinal fluid), gray matter, and white matter and ventricles using the effective segmentation method called level set segmentation. The features are extracted from these four regions of interest and classification of the dementia is performed using K-nearest neighbor.


Author(s):  
R.J. Mount ◽  
R.V. Harrison

The sensory end organ of the ear, the organ of Corti, rests on a thin basilar membrane which lies between the bone of the central modiolus and the bony wall of the cochlea. In vivo, the organ of Corti is protected by the bony wall which totally surrounds it. In order to examine the sensory epithelium by scanning electron microscopy it is necessary to dissect away the protective bone and expose the region of interest (Fig. 1). This leaves the fragile organ of Corti susceptible to physical damage during subsequent handling. In our laboratory cochlear specimens, after dissection, are routinely prepared by the O-T- O-T-O technique, critical point dried and then lightly sputter coated with gold. This processing involves considerable specimen handling including several hours on a rotator during which the organ of Corti is at risk of being physically damaged. The following procedure uses low cost, readily available materials to hold the specimen during processing ,preventing physical damage while allowing an unhindered exchange of fluids.Following fixation, the cochlea is dehydrated to 70% ethanol then dissected under ethanol to prevent air drying. The holder is prepared by punching a hole in the flexible snap cap of a Wheaton vial with a paper hole punch. A small amount of two component epoxy putty is well mixed then pushed through the hole in the cap. The putty on the inner cap is formed into a “cup” to hold the specimen (Fig. 2), the putty on the outside is smoothed into a “button” to give good attachment even when the cap is flexed during handling (Fig. 3). The cap is submerged in the 70% ethanol, the bone at the base of the cochlea is seated into the cup and the sides of the cup squeezed with forceps to grip it (Fig.4). Several types of epoxy putty have been tried, most are either soluble in ethanol to some degree or do not set in ethanol. The only putty we find successful is “DUROtm MASTERMENDtm Epoxy Extra Strength Ribbon” (Loctite Corp., Cleveland, Ohio), this is a blue and yellow ribbon which is kneaded to form a green putty, it is available at many hardware stores.


2020 ◽  
Vol 90 (3) ◽  
pp. 30502
Author(s):  
Alessandro Fantoni ◽  
João Costa ◽  
Paulo Lourenço ◽  
Manuela Vieira

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.


Author(s):  
J.R. Caradus ◽  
D.A. Clark

The New Zealand dairy industry recognises that to remain competitive it must continue to invest in research and development. Outcomes from research have ensured year-round provision of low-cost feed from pasture while improving productivity. Some of these advances, discussed in this paper, include the use of white clover in pasture, understanding the impacts of grass endophyte, improved dairy cow nutrition, the use of alternative forage species and nitrogen fertiliser to improve productivity, demonstration of the impact of days-in-milk on profitability, and the use of feed budgeting and appropriate pasture management. Keywords: dairy, profitability, research and development


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