Slime Mold optimization with hybrid deep learning enabled crowd-counting approach in video surveillance

Xu, Zheng; Jain, Deepak Kumar; Shamsolmoali, Pourya; Goli, Alireza; Neelakandan, Subramani; Jain, Amar

doi:10.1007/s00521-023-09083-x

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…It is not only a shortening of the diagnostic procedure; it marks an ascension in our grasp of these delicate situations, defining the boundary of medical imaging and diagnostics. This research intends to help doctors make more accurate diagnoses by highlighting the possibilities of combining high-resolution CT scans [ 33 ] with the CNN [ 34 ] and UNet [ 19 ] models in otolaryngology. In addition, this method facilitates the development of patient-specific treatment plans for auriculotemporal and ossicular disorders.…”

Section: Literature Surveymentioning

confidence: 99%

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

Cai,

Zhang,

Xie

et al. 2024

BMC Med Imaging

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Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.

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Section: Literature Surveymentioning

confidence: 99%