L-band InAs/InP quantum dash laser spatial OAM light modes classification under smoke environment: An image processing enhanced deep learning approach

Khan, M.Z.M.; Ragheb, A.M.; Masood, M.; Saif, W.; Esmail, M.A.; Iqbal, N.; Tareq, Q.; Almaiman, A.S.; Fathallah, H.; Alshebeili, S.

doi:10.1016/j.optlastec.2023.109933

Cited by 2 publications

(1 citation statement)

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“…The results emphasize the potential use machine learning techniques to characterize the pore structure of coal and other geological materials. Convolutional neural networks (CNNs) excel in recognizing complicated patterns, making them useful for applications such as nanoparticle characterization, facial recognition, medical picture diagnosis and autonomous vehicle navigation [7]. Deep Learning's versatility and scalability contribute to its extensive use, providing effective and dependable solutions to picture classification difficulties across several domains [8,9].…”

Section: Introductionmentioning

confidence: 99%

Classification of FESEM Images of Nanostructures by Noval Deep Learning Algorithm

Patil,

Choudapur

et al. 2024

Preprint

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SEM (Scanning Electron Microscopy) takes nanoscale pictures, whereas DL (Deep Learning) analyses data using neural networks. Image interpretation is streamlined by the collaboration of SEM and DL, which automates SEM image analysis and material characterisation. This integration improves productivity by quickly extracting relevant information from enormous datasets, highlighting subtle patterns that would otherwise be difficult to detect manually. Authors of this article consider 3 classes of SEM images of various magnifications and a total of 93 images is being produced for analysis and processing. Authors intend to enhance, categorize, and segment the pictures collected. SEM pictures are classified based on doping substance with the host material pure Cobalt Chromite, Neodymium doped Cobalt Chromite and Lanthanum doped Co-balt Chromite. Image quality and appearance are improved via augmentation techniques. The similarity and complexity of images in all three classes and the inclusion of images of different magnification posed a challenge in classification which hinder the accuracy rate of classification process. So authors use the statistical results of the tests to create semi-automatic method for classifying and labeling pictures generated by the SEM. Authors propose a low complexity algorithm, aimed to extract features and increase model performance. This approach offers efficiency gains by minimizing time and computational resources compared to pre-trained models, while maintaining consistent classification results. Achieved a training and testing accuracy of 100% and 78.26% respectively. SEM pictures are also classified using CNN and pre-trained models VGG16, Inception v3. To evaluate and compare performance, comparative studies are used to measure model correctness. SEM images are segmented into regions of interest using an integrated technique that combined the watershed and contours. A segmented picture is used to calculate the surface area of a Cobalt Chromite sample. The surface area of material is determined to be 41,02,628 nm 2 .

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Section: Introductionmentioning

confidence: 99%