Abstract
Colon cancer (CC) is a leading cause of mortality rate across worldwide. Early diagnosis of colon cancer prolongs human life and is also helpful in preventing the disease. Histopathological inspection is a frequently used technique to detect and diagnose it. Visual inspection of histopathological analysis needs more inspection time and the decision is based on clinicians’ subjective perceptions. Typically, machine learning techniques depend on conventional feature extraction which is time-consuming and laborious, and may not be suitable for a large amount of data. This work proposed a lightweight multi-headed convolutional neural network (MHCNN) for the classification of colon tissue using histopathological images. Authors have employed the first-time Tikhonov-based unsharp masking on colon tissue histopathological images. Tikhonov-based unsharp masking is used to highlight the high-frequency details (such as edges, corners, contours, etc.) of histopathological images. Then, the obtained unsharp mask-based histopathological images are given as input to the proposed MHCNN. Further, quantized aware training is applied to the proposed model to reduce the model size for efficient storage and speed up the training and testing time while maintaining high accuracy. The effectiveness of the developed model is compared with the other existing state-of-the-art methods. The proposed MHCNN achieved an average classification accuracy of 96.62%, precision of 97.48%, specificity of 97.46%, f1 score of 0.9664, and area under the curve of 0.9828. The quantized accuracy of 96.10% is achieved by the proposed network. Clinicians may install the developed network to validate the diagnosis in the hospitals.
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References
Alom MZ, Taha TM, Yakopcic C et al (2018) The history began from alexnet: a comprehensive survey on deep learning approaches. https://doi.org/10.48550/arXiv.1803.01164
Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. J Big Data 8(1):1–74. https://doi.org/10.1186/s40537-021-00444-8
Alzubaidi L, Fadhel MA, Al-Shamma O et al (2022) Robust application of new deep learning tools: an experimental study in medical imaging. Multimed Tools Appl, pp 1–29. https://doi.org/10.1007/s11042-021-10942-9
Boumaraf S, Liu X, Zheng Z et al (2021) A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomed Signal Process Control 63(102):192. https://doi.org/10.1016/j.bspc.2020.102192
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258. https://doi.org/10.48550/arXiv.1610.02357
Dai J (2020) Real-time and accurate object detection on edge device with tensorflow lite. In: Journal of physics: conference series. IOP publishing, pp 012114. https://doi.org/10.1088/1742-6596/1651/1/012114
Dhillon A, Verma GK (2020) Convolutional neural network: a review of models, methodologies and applications to object detection. Progress Artif Intell 9(2):85–112. https://doi.org/10.1007/s13748-019-00203-0
Ferlay J, Colombet M, Soerjomataram I et al (2019) Estimating the global cancer incidence and mortality in 2018: Globocan sources and methods. Int J Cancer 144(8):1941–1953. https://doi.org/10.1002/ijc.31937
Ghosh S, Bandyopadhyay A, Sahay S et al (2021) Colorectal histology tumor detection using ensemble deep neural network. Eng Appl Artif Intell 100(104):202. https://doi.org/10.1016/j.engappai.2021.104202
Gurcan MN, Boucheron LE, Can A et al (2009) Histopathological image analysis: a review. EEE Rev Biomed Eng 2:147–171. https://doi.org/10.1109/RBME.2009.2034865
Iandola FN, Han S, Moskewicz MW et al (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. https://doi.org/10.48550/arXiv.1602.07360
Jacob B, Kligys S, Chen B et al (2018) Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2704–2713. https://doi.org/10.48550/arXiv.1712.05877
Jia X, Mai X, Cui Y et al (2020) Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans Autom Sci Eng 17(3):1570–1584. https://doi.org/10.1109/TASE.2020.2964827
Kar NB, Babu KS, Sangaiah AK et al (2019) Face expression recognition system based on ripplet transform type ii and least square svm. Multimed Tools Appl 78:4789–4812. https://doi.org/10.1007/s11042-017-5485-0
Kather JN, Krisam J, Charoentong P et al (2019) Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med 16(1):e1002,730. https://doi.org/10.1371/journal.pmed.1002730
Kaya Y, GÜrsoy E (2023) A novel multi-head cnn design to identify plant diseases using the fusion of rgb images. Ecological Inf 75(101):998. https://doi.org/10.1016/j.ecoinf.2023.101998
Khan AM, Akram MU, Nazir S et al (2023) Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning. Biomed Signal Process Control 85(104):855. https://doi.org/10.1016/j.bspc.2023.104855
Kumar A, Vishwakarma A, Bajaj V et al (2021) Colon cancer classification of histopathological images using data augmentation. In: 2021 International conference on control, automation, power and signal processing (CAPS). IEEE, pp 1–5. https://doi.org/10.1109/CAPS52117.2021.9730704
Kumar A, Vishwakarma A, Bajaj V (2023) Crccn-net: automated framework for classification of colorectal tissue using histopathological images. Biomed Signal Process Control 79(104):172. https://doi.org/10.1016/j.bspc.2022.104172
Liang M, Ren Z, Yang J et al (2020) Identification of colon cancer using multi-scale feature fusion convolutional neural network based on shearlet transform. IEEE Access 8:208,969–208,977. https://doi.org/10.1109/ACCESS.2020.3038764
Liu Y, Chen X, Ward RK et al (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23(12):1882–1886. https://doi.org/10.1109/LSP.2016.2618776
Macenko M, Niethammer M, Marron JS et al (2009) A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International symposium on biomedical imaging: from nano to macro. IEEE, pp 1107–1110. https://doi.org/10.1109/ISBI.2009.5193250
Mehmood S, Ghazal TM, Khan MA et al (2022) Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access 10:25,657–25,668. https://doi.org/10.1109/ACCESS.2022.3150924
Mijwil MM (2021) Skin cancer disease images classification using deep learning solutions. Multimed Tools Appl 80(17):26,255–26,271. https://doi.org/10.1007/s11042-021-10952-7
Nguyen HG, Blank A, Lugli A et al (2020) An effective deep learning architecture combination for tissue microarray spots classification of h &e stained colorectal images. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp 1271–1274. https://doi.org/10.1109/ISBI45749.2020.9098636
Ohata EF, Chagas JVSd, Bezerra GM et al (2021) A novel transfer learning approach for the classification of histological images of colorectal cancer. J Supercomput 77(9):9494–9519. https://doi.org/10.1007/s11227-020-03575-6
Olgun G, Sokmensuer C, Gunduz-Demir C (2013) Local object patterns for the representation and classification of colon tissue images. IEEE J Biomed Health Inform 18(4):1390–1396. https://doi.org/10.1109/JBHI.2013.2281335
Qassim H, Verma A, Feinzimer D (2018) Compressed residual-vgg16 cnn model for big data places image recognition. In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, pp 169–175. https://doi.org/10.1109/CCWC.2018.8301729
Rathore S, Hussain M, Khan A (2015) Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Comput Biol Med 65:279–296. https://doi.org/10.1016/j.compbiomed.2015.03.004
Saito H, Aoki T, Aoyama K et al (2020) Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointestinal Endoscopy 92(1):144–151. https://doi.org/10.1016/j.gie.2020.01.054
Sardana H, Dogra N, Kanawade R et al (2022) Dynamic time warping based arrhythmia detection using photoplethysmography signals. Signal, Image and Video Processing, pp 1–9. https://doi.org/10.1007/s11760-022-02152-z
Sethy PK, Behera SK (2022) Automatic classification with concatenation of deep and handcrafted features of histological images for breast carcinoma diagnosis. Multimed Tools Appl 81(7):9631–9643. https://doi.org/10.1007/s11042-021-11756-5
Shaban M, Awan R, Fraz MM et al (2020) Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Trans Med Imaging 39(7):2395–2405. https://doi.org/10.1109/TMI.2020.2971006
Siegel RL, Miller KD, Wagle NS et al (2023) Cancer statistics, 2023. CA: A Cancer Journal for Clinicians 73(1):17–48. https://doi.org/10.3322/caac.21763
Singh S, Kumar R (2022) Breast cancer detection from histopathology images with deep inception and residual blocks. Multimed Tools Appl 81(4):5849–5865. https://doi.org/10.1007/s11042-021-11775-2
Sirinukunwattana K, Raza SEA, Tsang YW, et al (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196–1206. https://doi.org/10.1109/TMI.2016.2525803
Urban G, Tripathi P, Alkayali T et al (2018) Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155(4):1069–1078. https://doi.org/10.1053/j.gastro.2018.06.037
Wang C, Shi J, Zhang Q et al (2017) Histopathological image classification with bilinear convolutional neural networks. In: 2017 39th Annual international conference of the ieee engineering in medicine and biology society (EMBC). IEEE, pp 4050–4053. https://doi.org/10.1109/EMBC.2017.8037745
Wang D, Gao T, Zhang Y (2020) Image sharpening detection based on difference sets. IEEE Access 8:51,431–51,445. https://doi.org/10.1109/ACCESS.2020.2980774
Wang Y, Yang G, Li S et al (2023) Arrhythmia classification algorithm based on multi-head self-attention mechanism. Biomed Signal Process Control 79(104):206. https://doi.org/10.1016/j.bspc.2022.104206
Weis CA, Kather JN, Melchers S et al (2018) Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome. Diagn Pathol 13(1):1–12. https://doi.org/10.1186/s13000-018-0739-3
Yu C, Chen H, Li Y et al (2019) Breast cancer classification in pathological images based on hybrid features. Multimed Tools Appl 78:21,325–21,345. https://doi.org/10.1007/s11042-019-7468-9
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Kumar, A., Vishwakarma, A. & Bajaj, V. Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking. Multimed Tools Appl 83, 71753–71772 (2024). https://doi.org/10.1007/s11042-024-18357-y
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DOI: https://doi.org/10.1007/s11042-024-18357-y