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Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

Mohammed Abdulla Salim Al Husaini, Mohamed Hadi Habaebi, Teddy Surya Gunawan, Md Rafiqul Islam, Elfatih A. A. Elsheikh, F. M. Suliman

Neural Comput Appl. 2022; 34(1): 333–348. Published online 2021 Aug 7. doi: 10.1007/s00521-021-06372-1

PMCID:: PMC8349135

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A Deep Learning Framework for the Characterization of Thyroid Nodules from Ultrasound Images Using Improved Inception Network and Multi-Level Transfer Learning

O. A. Ajilisa, V. P. Jagathy Raj, M. K. Sabu

Diagnostics (Basel) 2023 Jul; 13(14): 2463. Published online 2023 Jul 24. doi: 10.3390/diagnostics13142463

PMCID:: PMC10378664

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Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization

Suresh Kolekar, Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri

Sensors (Basel) 2022 Dec; 22(24): 9677. Published online 2022 Dec 10. doi: 10.3390/s22249677

PMCID:: PMC9785663

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Hydrodynamic Cavitation on a Chip: A Tool to Detect Circulating Tumor Cells

Ilayda Namli, Seyedali Seyedmirzaei Sarraf, Araz Sheibani Aghdam, Gizem Celebi Torabfam, Ozlem Kutlu, Sibel Cetinel, Morteza Ghorbani, Ali Koşar

ACS Appl Mater Interfaces. 2022 Sep 14; 14(36): 40688–40697. Published online 2022 Sep 1. doi: 10.1021/acsami.2c12356

PMCID:: PMC9478945

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Deep Learning Based Analysis of Histopathological Images of Breast Cancer

Juanying Xie, Ran Liu, Joseph Luttrell, IV, Chaoyang Zhang

Front Genet. 2019; 10: 80. Published online 2019 Feb 19. doi: 10.3389/fgene.2019.00080

PMCID:: PMC6390493

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Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors

Yihan Fu, Zheng Zhao, Peng Lin

Entropy (Basel) 2024 Jan; 26(1): 48. Published online 2024 Jan 2. doi: 10.3390/e26010048

PMCID:: PMC10814965

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A Residual-Inception U-Net (RIU-Net) Approach and Comparisons with U-Shaped CNN and Transformer Models for Building Segmentation from High-Resolution Satellite Images

Batuhan Sariturk, Dursun Zafer Seker

Sensors (Basel) 2022 Oct; 22(19): 7624. Published online 2022 Oct 8. doi: 10.3390/s22197624

PMCID:: PMC9570988

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Deep learning-based approach for identification of diseases of maize crop

Md. Ashraful Haque, Sudeep Marwaha, Chandan Kumar Deb, Sapna Nigam, Alka Arora, Karambir Singh Hooda, P. Lakshmi Soujanya, Sumit Kumar Aggarwal, Brejesh Lall, Mukesh Kumar, Shahnawazul Islam, Mohit Panwar, Prabhat Kumar, R. C. Agrawal

Sci Rep. 2022; 12: 6334. Published online 2022 Apr 15. doi: 10.1038/s41598-022-10140-z

PMCID:: PMC9012772

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TrIncNet: a lightweight vision transformer network for identification of plant diseases

Pushkar Gole, Punam Bedi, Sudeep Marwaha, Md. Ashraful Haque, Chandan Kumar Deb

Front Plant Sci. 2023; 14: 1221557. Published online 2023 Jul 27. doi: 10.3389/fpls.2023.1221557

PMCID:: PMC10414585

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IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on Surface Electromyography

Xiangrui Wang, Lu Tang, Qibin Zheng, Xilin Yang, Zhiyuan Lu

Sensors (Basel) 2023 Jul; 23(13): 5775. Published online 2023 Jun 21. doi: 10.3390/s23135775

PMCID:: PMC10346255

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Learning Hierarchical Representations with Spike-and-Slab Inception Network

Weizheng Qiao, Xiaojun Bi

Sensors (Basel) 2021 Oct; 21(19): 6382. Published online 2021 Sep 24. doi: 10.3390/s21196382

PMCID:: PMC8512231

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3 directional Inception-ResUNet: Deep spatial feature learning for multichannel singing voice separation with distortion

DaDong Wang, Jie Wang, MingChen Sun

PLoS One. 2024; 19(1): e0289453. Published online 2024 Jan 29. doi: 10.1371/journal.pone.0289453

PMCID:: PMC10824424

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Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Aniwat Juhong, Bo Li, Cheng-You Yao, Chia-Wei Yang, Dalen W. Agnew, Yu Leo Lei, Xuefei Huang, Wibool Piyawattanametha, Zhen Qiu

Biomed Opt Express. 2023 Jan 1; 14(1): 18–36. Published online 2022 Dec 5. doi: 10.1364/BOE.463839

PMCID:: PMC9841988

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Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks

Min Dai, Wenjing Sun, Lixing Wang, Md Mehedi Hassan Dorjoy, Shanwen Zhang, Hong Miao, Liangxiu Han, Xin Zhang, Mingyou Wang

Front Plant Sci. 2023; 14: 1230886. Published online 2023 Aug 9. doi: 10.3389/fpls.2023.1230886

PMCID:: PMC10445539

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Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans

Harishwar Reddy Kasireddy, Udaykanth Reddy Kallam, Sowmitri Karthikeya Siddhartha Mantrala, Hemanth Kongara, Anshul Shivhare, Jayesh Saita, Sharanya Vijay, Raghu Prasad, Rajiv Raman, Chandra Sekhar Seelamantula

Diagnostics (Basel) 2023 Aug; 13(16): 2659. Published online 2023 Aug 12. doi: 10.3390/diagnostics13162659

PMCID:: PMC10453848

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Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16

Shan-Shan Su, Li-Ya Li, Yi Wang, Yuan-Zhe Li

Front Neurol. 2023; 14: 1111906. Published online 2023 Feb 14. doi: 10.3389/fneur.2023.1111906

PMCID:: PMC9971808

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Maize seed appearance quality assessment based on improved Inception-ResNet

Chang Song, Bo Peng, Huanyue Wang, Yuhong Zhou, Lei Sun, Xuesong Suo, Xiaofei Fan

Front Plant Sci. 2023; 14: 1249989. Published online 2023 Aug 24. doi: 10.3389/fpls.2023.1249989

PMCID:: PMC10484107

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MRF-IUNet: A Multiresolution Fusion Brain Tumor Segmentation Network Based on Improved Inception U-Net

Yongchao Jiang, Mingquan Ye, Peipei Wang, Daobin Huang, Xiaojie Lu

Comput Math Methods Med. 2022; 2022: 6305748. Published online 2022 Aug 4. doi: 10.1155/2022/6305748

PMCID:: PMC9371863

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The Bidirectional Gated Recurrent Unit Network Based on the Inception Module (Inception-BiGRU) Predicts the Missing Data by Well Logging Data

Youzhuang Sun, Junhua Zhang, Zhengjun Yu, Yongan Zhang, Zhen Liu

ACS Omega. 2023 Aug 1; 8(30): 27710–27724. Published online 2023 Jul 23. doi: 10.1021/acsomega.3c03677

PMCID:: PMC10399194

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Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Mengqing Qiu, Shouguo Zheng, Le Tang, Xujin Hu, Qingshan Xu, Ling Zheng, Shizhuang Weng

Foods. 2022 Feb; 11(4): 578. Published online 2022 Feb 17. doi: 10.3390/foods11040578

PMCID:: PMC8870785

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Figure 5

The classification accuracy (%) attained for the different feature sets generated using backward strategy. Feature set 1: DenseNet-201 + Inception V3 + Shuffle + Squeeze + ResNet-50 + MobileNet + Inception-ResNet-V2 + DarkNet-53 + Xception + NasNetMobile (7,194 Features); Feature set 2: DenseNet-201 + Inception V3 +Shuffle + Squeeze + ResNet-50 +MobileNet + Inception-ResNet-V2 + DarkNet-53 +Xception (6,666 Features); Feature set 3: DenseNet-201 + Inception V3 + Shuffle + Squeeze + ResNet-50 + MobileNet + Inception-ResNet-V2 + DarkNet-53 + NasNetMobile (6,170 Features); Feature set 4: DenseNet-201 + Inception V3 + Shuffle + Squeeze + ResNet-50 + MobileNet + Inception-ResNet-V2 + NasNetMobile (5,658 Features); Feature set 5: DenseNet-201 + Inception V3 +Shuffle + Squeeze + ResNet-50 + MobileNet+DarkNet-53 + NasNetMobile (5,402 Features); Feature set 6: DenseNet-201 + Inception V3 + Shuffle + Squeeze + ResNet-50 + DarkNet-53 + NasNetMobile (4,762 Features); Feature set 7: DenseNet-201 + Inception V3 + Shuffle + Squeeze + MobileNet + DarkNet-53 + NasNetMobile (4,378 Features); Feature set 8: DenseNet-201 + Inception V3 +Shuffle + ResNet-50 + MobileNet+DarkNet-53 + NasNetMobile (5,010 Features); Feature set 9: DenseNet-201 +Inception V3 + Squeeze + ResNet-50 + MobileNet + DarkNet-53 + NasNetMobile (5,030 Features); Feature set 10: DenseNet-201 + Shuffle + Squeeze + ResNet-50 + MobileNet + DarkNet-53 + NasNetMobile (4,378 Features); Feature set 11: Inception V3 + Shuffle + Squeeze + ResNet-50 + MobileNet + DarkNet-53 + NasNetMobile (4,442 Features).

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Front Neuroinform. 2021;15:663592.
Figure 3

Inception network modules, (a) inception network module 1 (InNet 1), (b) inception network module 2 (InNet 2), and (c) inception network module 3 (InNet 3), (F1 = 64 or 128 filters, F2 = 256 filters, F3 = 512 filters).

InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography

J Healthc Eng. 2021;2021:9975762.
Fig. 3

a Inception V4 Model, b Details of inception A, B and C layers, c Stem composition []

Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

Neural Comput Appl. 2021 Aug 7 ;34(1):333-348.
Figure 3.

An illustration of a dense inception module. Each dense inception module contains four basic inception modules, each of which is fully connected in a feed forward fashion.

A Deep Dense Inception Network for Protein Beta-Turn Prediction

Proteins. ;88(1):143-151.