CLASSIFICATION OF BRAIN TUMOR FROM MR IMAGES WITH LSTM-CNN HYBRID MODEL

Year 2024, , 63 - 81, 30.04.2024

Emrah Aslan

https://doi.org/10.54365/adyumbd.1391157

Abstract

Early detection of brain tumours is vital for effective treatment. Magnetic resonance imaging (MRI) is a fundamental tool for detecting brain tumours. There are many types of tumours such as glioma, meningioma, pituitary. Accurately determining the tumour type and making this determination is one of the most challenging aspects of classifying brain tumours. The use of artificial intelligence-based computer applications instead of traditional methods of disease detection can make significant contributions to experts in the detection of brain tumours. Especially deep learning methods are effective in disease detection based on the processing of medical images. In the literature, there are many deep learning-based approaches for categorising brain tumours. In this study, a model combining a CNN (Convolutional Neural Network) and a LSTM (Long Short Term Memory) deep learning layer is presented to detect brain tumours with MRI images. It is suggested that LSTM can support the feature extraction capabilities of CNN. In the experiments, it is found that the proposed LSTM-CNN model outperforms the standard CNN model. Using this model, an accuracy score of 98.1% was obtained in the detection of brain tumours. This result shows that it achieves a higher success compared to similar studies in the literature.

Keywords

CNN, LSTM, Tumour, MRI

LSTM-ESA HİBRİT MODELİ İLE MR GÖRÜNTÜLERİNDEN BEYİN TÜMÖRÜNÜN SINIFLANDIRILMASI

Abstract

Beyin tümörlerinin erken teşhisi, etkili bir tedavi için hayati öneme sahiptir. Manyetik rezonans (MR) görüntüleme, beyin tümörlerini tespit etmede temel bir araç olarak öne çıkmaktadır. Glioma, meningioma, pituitary gibi birçok tümör türü bulunmaktadır. Tümör türünü doğru bir şekilde belirlemek ve bu tespiti yapmak, beyin tümörlerini sınıflandırmanın en zorlu yönlerinden biridir. Geleneksel yöntemlerle hastalık tespiti yerine, yapay zekâ temelli bilgisayar uygulamalarının kullanılması, beyin tümörlerinin tespitinde uzmanlara önemli katkılar sağlayabilir. Özellikle derin öğrenme yöntemleri, medikal görüntülerin işlenmesine dayalı hastalık tespitinde etkili olmaktadır. Literatürde, beyin tümörlerini kategorize etmek için birçok derin öğrenme tabanlı yaklaşım bulunmaktadır. Bu çalışmada, MR görüntüleri ile beyin tümörlerini tespit etmek için bir ESA (Evrişimli Sinir Ağı) ve bir LSTM (Uzun Kısa Süreli Bellek) derin öğrenme katmanının birleştirilmiş olduğu bir model sunulmaktadır. LSTM'nin, ESA'nın özellik çıkarma yeteneklerini destekleyebileceği öne sürülmektedir. Yapılan deneylerde, önerilen LSTM-ESA modelinin standart ESA modelinden daha iyi performans gösterdiği belirlenmiştir. Bu modelin kullanılmasıyla, beyin tümörlerinin tespitinde %98,1 doğruluk skoru elde edilmiştir. Bu sonuç, literatürdeki benzer çalışmalarla karşılaştırıldığında daha yüksek bir başarı elde ettiğini göstermektedir.

Keywords

LSTM, Tümör, MR, ESA

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