On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Overview

Journal Arch Comput Methods Eng

Date 2023 Jun 1

PMID 37260910

Authors

Saeed Iqbal

Adnan N Qureshi

Jianqiang Li

Tariq Mahmood

Affiliations

Soon will be listed here.

Abstract

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

Citing Articles

Chinese medical named entity recognition utilizing entity association and gate context awareness.

Yan Y, Kang Y, Huang W, Cai X PLoS One. 2025; 20(2):e0319056.

PMID: 39999103 PMC: 11856514. DOI: 10.1371/journal.pone.0319056.

Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.

Alwakid G, Ul Haq F, Tariq N, Humayun M, Shaheen M, Alsadun M BMC Cardiovasc Disord. 2025; 25(1):123.

PMID: 39979842 PMC: 11844188. DOI: 10.1186/s12872-025-04550-w.

Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network.

Lin Z, Yang J, Lian Y, Chen Y, Huang Z, Ning K Sci Rep. 2025; 15(1):5150.

PMID: 39934359 PMC: 11814336. DOI: 10.1038/s41598-025-89534-8.

Breast cancer classification based on hybrid CNN with LSTM model.

Kaddes M, Ayid Y, Elshewey A, Fouad Y Sci Rep. 2025; 15(1):4409.

PMID: 39910136 PMC: 11799331. DOI: 10.1038/s41598-025-88459-6.

Boosting skin cancer diagnosis accuracy with ensemble approach.

Natha P, Tera S, Chinthaginjala R, Rab S, Narasimhulu C, Kim T Sci Rep. 2025; 15(1):1290.

PMID: 39779772 PMC: 11711234. DOI: 10.1038/s41598-024-84864-5.

References

Pires F, Costa C, Dias P . On the Use of Virtual Reality for Medical Imaging Visualization. J Digit Imaging. 2021; 34(4):1034-1048. PMC: 8455774. DOI: 10.1007/s10278-021-00480-z. View

Huang R, Lin M, Dou H, Lin Z, Ying Q, Jia X . Boundary-rendering network for breast lesion segmentation in ultrasound images. Med Image Anal. 2022; 80:102478. DOI: 10.1016/j.media.2022.102478. View

Han C, Lin J, Mai J, Wang Y, Zhang Q, Zhao B . Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Med Image Anal. 2022; 80:102487. DOI: 10.1016/j.media.2022.102487. View

Gunasekaran K, Pitchai R, Chaitanya G, Selvaraj D, Annie Sheryl S, S Almoallim H . A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs. Biomed Res Int. 2022; 2022:3163496. PMC: 9197616. DOI: 10.1155/2022/3163496. View

Brosch T, Tam R . Manifold learning of brain MRIs by deep learning. Med Image Comput Comput Assist Interv. 2014; 16(Pt 2):633-40. DOI: 10.1007/978-3-642-40763-5_78. View

Wang A, Cunningham I, Danielsson M, Fahrig R, Flohr T, Hoeschen C . Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences. J Med Imaging (Bellingham). 2022; 9(Suppl 1):012205. PMC: 8926876. DOI: 10.1117/1.JMI.9.S1.012205. View

Ghafoorian M, Karssemeijer N, Heskes T, Bergkamp M, Wissink J, Obels J . Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin. Neuroimage Clin. 2017; 14:391-399. PMC: 5322213. DOI: 10.1016/j.nicl.2017.01.033. View

Ho C, Soon D, Caals K, Kapur J . Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin Radiol. 2019; 74(5):329-337. DOI: 10.1016/j.crad.2019.02.005. View

Shin H, Choi G, Shon O, Kim G, Chang M . Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image. BMC Musculoskelet Disord. 2022; 23(1):510. PMC: 9150332. DOI: 10.1186/s12891-022-05468-6. View

10.

Huang R, Ying Q, Lin Z, Zheng Z, Tan L, Tang G . Extracting keyframes of breast ultrasound video using deep reinforcement learning. Med Image Anal. 2022; 80:102490. DOI: 10.1016/j.media.2022.102490. View

11.

Durstewitz D, Koppe G, Meyer-Lindenberg A . Deep neural networks in psychiatry. Mol Psychiatry. 2019; 24(11):1583-1598. DOI: 10.1038/s41380-019-0365-9. View

12.

Saini M, Susan S . Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets. Comput Biol Med. 2022; 149:105989. DOI: 10.1016/j.compbiomed.2022.105989. View

13.

Gulshan V, Peng L, Coram M, Stumpe M, Wu D, Narayanaswamy A . Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016; 316(22):2402-2410. DOI: 10.1001/jama.2016.17216. View

14.

Yalcin S, Vural H . Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks. Comput Biol Med. 2022; 149:105941. DOI: 10.1016/j.compbiomed.2022.105941. View

15.

Hinton G, Osindero S, Teh Y . A fast learning algorithm for deep belief nets. Neural Comput. 2006; 18(7):1527-54. DOI: 10.1162/neco.2006.18.7.1527. View

16.

Gu Y, Chi J, Liu J, Yang L, Zhang B, Yu D . A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput Biol Med. 2021; 137:104806. DOI: 10.1016/j.compbiomed.2021.104806. View

17.

Sadik F, Dastider A, Subah M, Mahmud T, Fattah S . A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images. Comput Biol Med. 2022; 149:105806. PMC: 9295386. DOI: 10.1016/j.compbiomed.2022.105806. View

18.

Wei T, Aviles-Rivero A, Wang S, Huang Y, Gilbert F, Schonlieb C . Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations. Med Image Anal. 2022; 82:102618. DOI: 10.1016/j.media.2022.102618. View

19.

Jin C, Udupa J, Zhao L, Tong Y, Odhner D, Pednekar G . Object recognition in medical images via anatomy-guided deep learning. Med Image Anal. 2022; 81:102527. DOI: 10.1016/j.media.2022.102527. View

20.

Li L, Liu Z, Huang H, Lin M, Luo D . Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists. Thorac Cancer. 2018; 10(2):183-192. PMC: 6360226. DOI: 10.1111/1759-7714.12931. View