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Semantic segmentation of breast cancer images using DenseNet with proposed PSPNet

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Abstract

For early detection of cancer tumors, the semantic segmentation based technique is proposed because the existing numerous methods fail while classifying due to accuracy and ineffectual decision-making. Therefore, in this paper, the hybrid semantic segmentation networks are introduced. In the beginning, the input image sets are applied into the pre-processing phase and after that, subject to the process of segmentation. The pre-processing process of image contrast enhancement is done by Adaptive Local Gamma Correction (ALGC). The semantic segmentation topology is done by using the hybrid network of the DenseNet-121 model with Attention based pyramid scene parsing network (Att-PSPnet). The feature map extraction and scene parsing are handled by the Att-PSPnet network. The Attention Gate mechanism is introduced to improve the quality of the high-dimensional hidden layer features by highlighting the useful information, and unnecessary information and noise are suppressed. To make the efficient decision and enhance prediction accuracy, the pyramid dilated convolution module (PDM) is a branch of the attention-based pyramid pooling module that enlarges the receptive field to extract global information. Additionally, the global average pooling (GAP) layer is introduced at the output of the feature map. The performance of the proposed method is validated using the Google Colab environment with a histologically confirmed dataset. The experimental results are compared with existing methods like FCN, Unet, and PSPNet in terms of IoU, accuracy, precision, recall, F1 score, IoU, and more. The proposed method achieves 94.68% prediction accuracy which is higher than the existing approaches.

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References

  1. Turan S, Bilgin G (2019) Semantic nuclei segmentation with deep learning on breast pathology images. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science, Istanbul, Turkey, pp 1–4

  2. Natarajan VA, Kumar MS, Patan R, Kallam S, Mohamed MYN (2020) Segmentation of Nuclei in Histopathology images using Fully Convolutional Deep Neural Architecture. In: International Conference on Computing and Information Technology, Tabuk, Saudi Arabia, pp 1–7

  3. Zebari DA, Zeebaree DQ, Abdulazeez AM, Haron H, Hamed HNA (2020) Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access 8(1):203097–203116

    Article  Google Scholar 

  4. Zohair AA, Shamil AA, Sulong G (2015) Latest methods of image enhancement and restoration for computed tomography: a concise review. Appl Med Inform 36(1):1–12

    Google Scholar 

  5. Acharya A, Giri AV (2020) Contrast improvement using local gamma correction. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), Coimbatore, India, pp 110–114

  6. Altaf MM (2021) A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks. Math Biosci Eng 18(5):5029–5046

    Article  MathSciNet  Google Scholar 

  7. Akter O, Moni MA, Islam MM, Quinn JMW, Kamal AHM (2020) Lung cancer detection using enhanced segmentation accuracy. Appl Intell 51(6):3391–3404

    Article  Google Scholar 

  8. Rezae M, Yang H, Meinel C (2019) Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimed Tools Appl 79(2):15329–15348

    Google Scholar 

  9. Heidler K, Mou L, Baumhoer C, Dietz A, Zhu XX (2021) HED-UNet: combined segmentation and edge detection for monitoring the Antarctic coastline. IEEE Trans Geosci Remote Sens 60:1–14

    Article  Google Scholar 

  10. Lee H, Park J, Hwang JY (2020) Channel attention module with multiscale grid average pooling for breast cancer segmentation in an ultrasound image. IEEE Trans Ultrason Ferroelectr Freq Control 67(7):1344–1353

    Google Scholar 

  11. Bouget D, Jørgensen A, Kiss G, Leira HO, Lango T (2019) Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging. Int J Comput Assist Radiol Surg 14(6):977–986

    Article  Google Scholar 

  12. Guo Z, Guangming Wu, Song X, Yuan W, Chen Qi, Zhang H, Shi X, Mingzhou Xu, Yongwei Xu, Shibasaki R, Shao X (2019) Super-resolution integrated building semantic segmentation for multi-source remote sensing imagery. IEEE Access 7:99381–99397

    Article  Google Scholar 

  13. Ahmed L, Iqbal MM, Aldabbas H, Khalid S, Saleem Y, Saeed S (2020) Images data practices for semantic segmentation of breast cancer using deep neural network. J Ambient IntellHumaniz Comput 13:1–17

  14. Ma Z, Li J, Salemi H, Arnold C, Knudsen BS, Gertych A, Ing N (2018) Semantic segmentation for prostate cancer grading by convolutional neural networks. In Medical Imaging 2018: Digital Pathology, vol. 10581. United States

  15. Sha Z, Lin Hu, Rouyendegh BD (2020) Deep learning and optimization algorithms for automatic breast cancer detection. Int J Imaging Syst Technol 30(2):495–506

    Article  Google Scholar 

  16. Soulami KB, Kaabouch N, Saidi MN, Tamtaoui A (2021) Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control 66(102481):1–12

  17. Cho SW, Koo JH, Arsalan M, Park KR (2020) Semantic segmentation with low light images by modified CycleGAN-based image enhancement. IEEE Access 8(93561):93585

    Google Scholar 

  18. Dhalla S, Maqbool J, Mann TS, Gupta A, Mittal A, Aggarwal P et al (2023) Semantic segmentation of palpebral conjunctiva using predefined deep neural architectures for anemia detection. Procedia Comput Sci 218:328–337

  19. Cho SW, Baek NR, Park KR (2022) Deep learning-based multi-stage segmentation method using ultrasound images for breast cancer diagnosis. J King Saud Univ-Comput Inform Sci 34(10):10273–10292

    Google Scholar 

  20. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp 4700–4708

  21. Cui B, Chen X, Yan Lu (2020) Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection. IEEE Access 8:116744–116755

    Article  Google Scholar 

  22. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp 6230–6239

  23. Maji D, Sigedar P, Singh M (2022) Attention Res-UNet with guided decoder for semantic segmentation of brain tumors. Biomed Signal Process Control 71:103077

    Article  Google Scholar 

  24. Liu P, Hong Y, Liu Y (2019) Deep differential convolutional network for single image super-resolution. IEEE Access 7:37555–37564

    Article  Google Scholar 

  25. Gridach M (2021) PyDiNet: pyramid dilated network for medical image segmentation. Neural Netw 140:274–281

    Article  Google Scholar 

  26. Gomez-Flores W, de Albuquerque Pereira WC (2020) A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound. Comput Biol Med 126(104036):1–28

  27. Tang W, Dongsheng Zou Su, Yang JS, Dan J, Song G (2020) A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Comput Appl 32(2):6769–6778

    Article  Google Scholar 

  28. Soulami KB, Kaabouch N, Saidi MN, Tamtaoui A (2021) Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using UNet model based-semantic segmentation. Biomed Signal Process Control 66:102481

    Article  Google Scholar 

  29. AlEisa HN, Touiti W, Ali ALHussan A, Ben Aoun N, Ejbali R, Zaied M, Saadia A (2022) Breast cancer classification using FCN and beta wavelet autoencoder. Comput Intell Neurosci 2022

  30. Qiu Y, Yan S, Gundreddy RR, Wang Y, Cheng S, Liu H, Zheng B (2017) A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J X-Ray Sci Technol 25(5):751–763

    Google Scholar 

  31. Samala RK, Chan H-P, Hadjiiski LM, Helvie MA, Cha KH, Richter CD (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms’. Phys Med Biol 62(23):8894–8908

    Article  Google Scholar 

  32. Nascimento CDL, Silva SDDS, Silva TAD, Pereira WCDA, Costa MGF, Costa Filho CFF (2016) Breast tumor classification in ultrasound images using support vector machines and neural networks. Res Biomed Eng 32(3):283–292

    Article  Google Scholar 

  33. Youk JH, Gweon HM, Son EJ (2017) Shear-wave elastography in breast ultrasonography: the state of the art. Ultrasonography 36(4):300–309

    Article  Google Scholar 

  34. Amit G, Ben-Ari R, Hadad O, Monovich E, Granot N, Hashoul S (2017) Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches. In Proc Med Imag, Comput- Aided Diagnosis 10134:374–379

  35. Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171

    Article  Google Scholar 

  36. Rasti R, Teshnehlab M, Phung SL (2017) Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recognit 72:381–390

    Article  Google Scholar 

  37. Altini N, Puro E, Taccogna MG, Marino F, De Summa S, Saponaro C., ... Bevilacqua V (2023) Tumor cellularity assessment of breast histopathological slides via instance segmentation and pathomic features explainability. Bioengineering 10(4): 396

  38. Das N, Saha S, Nasipuri M, Basu S, Chakraborti T (2023) Deep-Fuzz: a synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology. PloS One 18(6):e0286862

    Article  Google Scholar 

  39. Zhao Z, Pang F, Liu Y, Liu Z, Ye C (2023) Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations. arXiv preprint arXiv:2302.08050

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad (IIT Hyderabad), Kandi, India

    Suresh Samudrala

  2. Department of Computer Science and Engineering & Dean (Public and Corporate Relations), Indian Institute of Technology Hyderabad (IIT Hyderabad), Kandi, India

    C. Krishna Mohan

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Correspondence to Suresh Samudrala.

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Samudrala, S., Mohan, C.K. Semantic segmentation of breast cancer images using DenseNet with proposed PSPNet. Multimed Tools Appl 83, 46037–46063 (2024). https://doi.org/10.1007/s11042-023-17411-5

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