Feyzi Ferat ATEŞ, Abidin ÇALIŞKAN, Mesut TOĞAÇAR

Meme Kanserinin Tespiti için Yapay Zekâ Tabanlı Hibrit Bir Model Önerisi

Meme kanseri genellikle memenin lobül, kanal ve bağ dokusu bölgelerinde meydana gelen ve bu bölgelerdeki hücrelerin anormal bir şekilde hareketinden meydana gelen kanser türüdür. Genel olarak bayanlar arasında en sık görülen kanser türleri arasında yer almaktadır. Hastalık erkenden fark edilmeği zaman kan ve lenf damarları yoluyla diğer organlara kanserli hücreler etki edebilir (metastaz durumu). Dolayısıyla meme kanserinin erken tanı ve tedavisi önemlidir. Bu çalışmada meme kanserinin iyi huylu ve kötü huylu türleri arasında sınıflandırma yapabilen yapay zekâ tabanlı erken tanı sistemi önerilmiştir. Önerilen yaklaşımda artık bloklu evrişimsel sinir ağı modelleri kullanıldı. ResNet modellerinin son katmanına yeni bir tam bağlantılı katman eklenerek tür tabanlı öznitelikler çıkartıldı. Bir sonraki aşamada tam bağlantılı katmanlardan elde edilmiş öznitelikler birleştirilerek yeni bir özellik seti oluşturuldu. Sınıflandırma sürecinde softmax ve makine öğrenme yöntemleri (destek vektör makineleri, en yakın komşu yöntemi, vb.) kullanıldı. Önerilen yaklaşım ile sınıflandırma sürecinde kullanılan tüm yöntemlerden %100 genel doğruluk başarısı elde edildi. Bu çalışmada tür tabanlı tam bağlantılı katmanların elde edilmesi ve birleştirilmesi deneysel analizlerin performansını olumlu etkilediği gözlenmiştir.

Anahtar Kelimeler:

Yapay zekâ, özellik seçimi, makine öğrenmesi, meme kanseri, sınıflandırma.

An Artificial Intelligence Based Hybrid Model Proposal for the Detection of Breast Cancer

Breast cancer is a type of cancer that usually occurs in the lobule, duct and connective tissue regions of the breast and is caused by the abnormal movement of cells in these regions. It is among the most common types of cancer among women in general. When the disease is detected early, cancerous cells can affect other organs through the blood and lymphatic vessels (metastasis state). Therefore, early diagnosis and treatment of breast cancer is important. In this study, an artificial intelligence-based early diagnosis system that can classify between benign and malignant types of breast cancer is proposed. Residual blocky neural network models are used in the proposed approach. Type-based features were extracted by adding a new fully connected layer to the last layer of ResNet models. In the next step, a new feature set was created by combining the features obtained from the fully connected layers. Softmax and machine learning methods (support vector machines, nearest neighbor method, etc.) were used in the classification process. With the proposed approach, 100% overall accuracy was obtained from all the methods used in the classification process. In this study, it was observed that obtaining and combining type-based fully connected layers positively affected the performance of experimental analyzes.

Keywords:

Artificial intelligence, feature selection, machine learning, breast cancer, classification,

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G. Cooper, The Development and Causes of Cancer, 2nd ed., Sinauer Associates, Sunderland (MA), 2000. https://www.ncbi.nlm.nih.gov/books/NBK9963/.
A. Patel, Benign vs Malignant Tumors, JAMA Oncol. 6 (2020) 1488. doi:10.1001/jamaoncol.2020.2592.
N.S. Ariffin, RUNX1 as a Novel Molecular Target for Breast Cancer, Clin. Breast Cancer. 22 (2022) 499–506. doi:https://doi.org/10.1016/j.clbc.2022.04.006.
M.N. Uddin, X. Wang, Identification of Breast Cancer Subtypes Based on Gene Expression Profiles in Breast Cancer Stroma, Clin. Breast Cancer. 22 (2022) 521–537. doi:https://doi.org/10.1016/j.clbc.2022.04.001.
K. Kersting, Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines, Front. Big Data. 1 (2018). doi:10.3389/fdata.2018.00006.
A. Gastounioti, S. Desai, V.S. Ahluwalia, E.F. Conant, D. Kontos, Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review, Breast Cancer Res. 24 (2022) 14. doi:10.1186/s13058-022-01509-z.
M.A. Naji, S. El Filali, K. Aarika, E.L.H. Benlahmar, R.A. Abdelouhahid, O. Debauche, Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis, Procedia Comput. Sci. 191 (2021) 487–492. doi:https://doi.org/10.1016/j.procs.2021.07.062.
C.G. Yedjou, S.S. Tchounwou, R.A. Aló, R. Elhag, B. Mochona, L. Latinwo, Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification., Int. J. Sci. Acad. Res. 2 (2021) 3081–3086.
A. Kazemi, M. Ebrahim, A. Sheikhahmadi, M. Khodamoradi, A new parallel deep learning algorithm for breast cancer classification, 12 (2021) 1269–1282.
D.A. Ragab, M. Sharkas, S. Marshall, J. Ren, Breast cancer detection using deep convolutional neural networks and support vector machines, PeerJ. 7 (2019) e6201. doi:10.7717/peerj.6201.
F.A. Spanhol, L.S. Oliveira, C. Petitjean, L. Heutte, A Dataset for Breast Cancer Histopathological Image Classification, IEEE Trans. Biomed. Eng. 63 (2016) 1455–1462. doi:10.1109/TBME.2015.2496264.
K. Muzaki, BreaKHis 400X, Kaggle Web. (2020). https://www.kaggle.com/datasets/forderation/breakhis-400x?resource=download.
W. Alsaggaf, Z. Cömert, M. Nour, K. Polat, H. Brdesee, M. Toğaçar, Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals, Appl. Acoust. 167 (2020) 107429. doi:10.1016/j.apacoust.2020.107429.
M. Toğaçar, B. Ergen, Z. Cömert, Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks, Med. Biol. Eng. Comput. 59 (2021) 57–70. doi:10.1007/s11517-020-02290-x.
A. Diker, Z. Comert, E. Avci, M. Togacar, B. Ergen, A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification, in: 2019 1st Int. Informatics Softw. Eng. Conf., IEEE, 2019: pp. 1–6. doi:10.1109/UBMYK48245.2019.8965506.
E. Başaran, A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms, Comput. Biol. Med. 148 (2022) 105857. doi:https://doi.org/10.1016/j.compbiomed.2022.105857.
A. Venkata, S. Abhishek, Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset, 10 (2022) 176–181.
S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, Aggregated Residual Transformations for Deep Neural Networks, (2016). doi:10.1109/cvpr.2017.634.
B. Gao, L. Pavel, On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning, (2017). http://arxiv.org/abs/1704.00805.
Y. Luo, Y. Wong, M. Kankanhalli, Q. Zhao, Softmax: Improving Intraclass Compactness and Interclass Separability of Features, IEEE Trans. Neural Networks Learn. Syst. 31 (2020) 685–699. doi:10.1109/tnnls.2019.2909737.
I. Olier, O.I. Orhobor, T. Dash, A.M. Davis, L.N. Soldatova, J. Vanschoren, R.D. King, Transformational machine learning: Learning how to learn from many related scientific problems, Proc. Natl. Acad. Sci. 118 (2021) e2108013118. doi:10.1073/pnas.2108013118.
H. Wu, L. Wang, Z. Zhao, C. Shu, C. Lu, Support Vector Machine based Differential Pulse-width Pair Brillouin Optical Time Domain Analyzer, IEEE Photonics J. 10 (2018) 1–11. doi:10.1109/jphot.2018.2858235.
A. Niwatkar, Y.K. Kanse, Feature Extraction using Wavelet Transform and Euclidean Distance for speaker recognition system, in: 2020 Int. Conf. Ind. 4.0 Technol., 2020: pp. 145–147. doi:10.1109/I4Tech48345.2020.9102683.
H. Mandelkow, J.A. de Zwart, J.H. Duyn, Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli, Front. Hum. Neurosci. 10 (2016). doi:10.3389/fnhum.2016.00128.
E. Başaran, Z. Cömert, Y. Çelik, Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images, Neural Comput. Appl. 34 (2022) 6027–6038. doi:10.1007/s00521-021-06810-0.
H. Polat, M. Turkoglu, O. Polat, Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET, IET Commun. 14 (2020) 4089–4100. doi:10.1049/iet-com.2020.0477.