Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model

Bengs M, Westermann S, Gessert N, Eggert D, Gerstner AOH, Mueller NA, Betz C, Laffers W, Schlaefer A. ‘Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection.’ Proc SPIE. 2020;11314:369–74.

Google Scholar 

Tayade HM. Early detection of laryngeal cancer using multiple instance learning based neural network. Doctoral dissertation, National College of Ireland, Dublin, Ireland, 2020.

Zhou X, Tang C, Huang P, Mercaldo F, Santone A, Shao Y. LPCANet: classification of laryngeal cancer histopathological images using a CNN with position attention and channel attention mechanisms. Interdiscipl Sci Comput Life Sci. 2021;13(4):666–82.

Article  Google Scholar 

Esmaeili N, Sharaf E, Ataide EJG, Illanes A, Boese A, Davaris N, Arens C, Navab N, Friebe M. Deep convolution neural network for laryngeal cancer classification on contact endoscopy-narrow band imaging. Sensors. 2021;21(23):8157.

Article  Google Scholar 

Kim H, Jeon J, Han YJ, Joo Y, Lee J, Lee S, Im S. Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. J Clin Med. 2020;9(11):3415.

Article  Google Scholar 

Bur AM, Zhang T, Chen X, Kavookjian H, Kraft S, Karadaghy O, Farrokhian N, Mussatto C, Penn J, Wang G. Interpretable computer vision to detect and classify structural laryngeal lesions in digital flexible laryngoscopic images. Otolaryngol-Head Neck Surg. 2023. https://doi.org/10.1002/ohn.411.

Article  Google Scholar 

Wellenstein DJ, Woodburn J, Marres HAM, van den Broek GB. ‘Detection of laryngeal carcinoma during endoscopy using artificial intelligence.’ Head Neck. 2023;45(9):2217–26.

Article  Google Scholar 

Meyer-Veit F, Rayyes R, Gerstner AOH, Steil J. Hyperspectral wavelength analysis with U-Net for larynx cancer detection. In: Proceedings European Symposium on Artificial Neural Networks (ESANN), Computational Intelligence and Machine Learning, Bruges, Belgium. 2022.

Xiong H, Lin P, Yu J-G, Ye J, Xiao L, Tao Y, Jiang Z, Lin W, Liu M, Xu J, Hu W, Lu Y, Liu H, Li Y, Zheng Y, Yang H. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine. 2019;48:92–9.

Article  Google Scholar 

He Y, Cheng Y, Huang Z, Xu W, Hu R, Cheng L, He S, Yue C, Qin G, Wang Y, Zhong Q. ‘A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis.’ Ann Transl Med. 2021;9(24):1797.

Article  Google Scholar 

Wang W, Liu Y, Wu J. Early diagnosis of oral cancer using a hybrid arrangement of deep belief network and combined group teaching algorithm. Sci Rep. 2023;13(1):22073.

Article  Google Scholar 

Bhattacharya D, Behrendt F, Felicio-Briegel A, Volgger V, Eggert D, Betz C, Schlaefer A. Learning robust representation for laryngeal cancer classification in vocal folds from narrow band images. In Medical Imaging with Deep Learning. 2022.

Joseph JS, Vidyarthi A, Singh VP. An improved approach for initial stage detection of laryngeal cancer using effective hybrid features and ensemble learning method. Multimed Tools Appl. 2023. https://doi.org/10.1007/s11042-023-16077-3.

Article  Google Scholar 

Sachane MN, Patil SA. Adaptive Spotted Hyena Optimizer-enabled Deep QNN for Laryngeal Cancer Classification. In: 2022 International Conference on Edge Computing and Applications (ICECAA). 2022, pp. 1025–1032. IEEE.

Huang Q, Ding H, Razmjooy N. Optimal deep learning neural network using ISSA for diagnosing oral cancer. Biomed Signal Process Control. 2023;84:104749.

Article  Google Scholar 

Deif MA, Attar H, Amer A, Elhaty IA, Khosravi MR, Solyman AA. Diagnosis of oral squamous cell carcinoma using deep neural networks and binary Particle Swarm optimization on histopathological images: an AIoMT approach. Comput Intell Neurosci. 2022. https://doi.org/10.1155/2022/6364102.

Article  Google Scholar 

Song S, Ren X, He J, Gao M, Wang JN, Wang B. An optimal hierarchical approach for oral cancer diagnosis using rough set theory and an amended version of the competitive search algorithm. Diagnostics. 2023;13(14):2454.

Article  Google Scholar 

Sophia NA, Jiji GW. Classification of acute pathology for vocal cord using advanced multi-resolution algorithm. Int J Patt Recogn Artif Intell. 2022. https://doi.org/10.1142/S0218001422580046.

Article  Google Scholar 

Shamrat FJM, Akter S, Azam S, Karim A, Ghosh P, Tasnim Z, Hasib KM, De Boer F, Ahmed K. AlzheimerNet: an effective deep learning-based proposition for Alzheimer’s disease stages classification from functional brain changes in magnetic resonance images. IEEE Access. 2023;11:16376–95.

Article  Google Scholar 

Ali AM, Mohammed MA. A comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges. Int J Math Stat Comput Sci. 2024;2:114–67.

Article  Google Scholar 

Mohammed MA, Lakhan A, Abdulkareem KH, Deveci M, Dutta AK, Memon S, Marhoon HA, Martinek R. Federated-reinforcement learning-assisted IoT consumers system for kidney disease images. IEEE Trans Consumer Electron. 2024. https://doi.org/10.1109/TCE.2024.3384455.

Article  Google Scholar 

Mohammed MA, Lakhan A, Abdulkareem KH, Garcia-Zapirain B. Federated auto-encoder and XGBoost schemes for multi-omics cancer detection in distributed fog computing paradigm. Chemom Intell Lab Syst. 2023;241:104932.

Article  Google Scholar 

Mukhlif AA, Al-Khateeb B, Mohammed M. Classification of breast cancer images using new transfer learning techniques. Iraqi J Comput Sci Math. 2023;4(1):167–80.

Google Scholar 

Qi C, Sandroni M, Westergaard JC, Sundmark EHR, Bagge M, Alexandersson E, Gao J. In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging. Comput Electron Agric. 2023;205:107585.

Article  Google Scholar 

留言 (0)

沒有登入
gif