Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Ayaz, Hamail; Sohaib, Ahmed; Yasir, Muhammad Naveed; Zaidan, Martha Arbayani; Ali, Mohsin; Khan, Muhammad Hussain; Saleem, Zainab

doi:10.3390/app10196862

Cited by 20 publications

(16 citation statements)

References 46 publications

(62 reference statements)

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“…HSI has been adopted in several real-world applications including but not limited to the atmosphere, environmental, urban, agriculture, geological and mineral exploration, coastal zone, marine, forestry (i.e. track forest health), water quality and surface contamination, inland waters, and wetlands, snow and ice, biological, medical contexts, and food processing [3]- [8]. There are also several military applications in camouflage, landmine detection, and littoral zone mapping.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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184

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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the stateof-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

184

View full text Add to dashboard Cite

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“…Higher-order derivatives cause a reduction in signal-to-noise ratio (SNR). Savitzky Golay filter is one of the mathematical derivative methods that are better than others due to its soothing property [ 42 , 43 , 44 , 45 ]. The Savitzky Golay filter is based on polynomial fitting on data points depending upon window size.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging for Bloodstain Identification

Zulfiqar

Ahmad

Sohaib

et al. 2021

Sensors

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Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

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“…Computer-aided diagnosis (CAD) has become an integral part of radiology and clinical diagnosis since the past decades when the emergence of sophisticated imaging techniques, like X-ray, ultrasound, and MRI, became evident. As such, valuable information is made available to experts, which requires professional evaluation and analysis for detection of abnormalities and classification of pathological traits [1].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID‐19 in CT Images

et al. 2021

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The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.

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Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Cited by 20 publications

References 46 publications

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging for Bloodstain Identification

Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID‐19 in CT Images

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