Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm

Siar, Masoumeh; Teshnehlab, Mohammad

doi:10.1109/iccke48569.2019.8964846

Cited by 154 publications

(54 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…YÖK Tez Merkezi'nden edinilen verilere göre; 2010-2020 yılları arasında görüntü işleme konusunda yazılan 60 doktora tezinden 24 tanesinin biyomedikal alanda olduğu tespit edilmiştir [13]. Ayrıca son yıllardaki literatür incelendiğinde ortopedi dalının yanında görüntü işleme konusunun göz hastalıkları, nöroloji, göğüs hastalıkları, dermatoloji, kardiyoloji gibi diğer tıp dallarında da uygulandığı görülmektedir [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Literatür çAlışmalarıunclassified

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Ergün¹,

Güney²,

Ergün³

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

Son yılların en popüler konularından olan derin öğrenme, pek çok alanda olduğu gibi biyomedikal alanda da sıkça kullanılmaktadır. Çeşitli görüntüleme yöntemleri ile elde edilen görüntüler kullanılarak hastalık ve kırık tespiti, biyolojik veri kestirimi, doku ve organ bölütlemesi, eksik veri tamamlanması gibi nice uygulama derin öğrenme algoritmaları sayesinde başarılı bir şekilde gerçekleştirilmektedir. Ancak bahsi geçen uygulamaların çok büyük bir çoğunluğu beşeri hekimlikte yapılırken, veteriner tıp geri planda bırakılmıştır. Özellikle literatürde bu alandaki eksikliğin fark edilmesi bu çalışma konusunun en büyük motivasyon kaynağı olmuştur. Bu çalışmada, Ankara Büyükşehir Belediyesi Sokak Hayvanları Geçici Bakım Evi'nden alınan, köpeklere ait röntgenleri içeren geniş kapsamlı bir veri seti, derin öğrenme algoritmaları ile işlenmiştir. Amaç, köpeklere ait X-Ray görüntülerinden uzun kemiğin çeşidinin belirlenmesidir. Biyomedikal görüntü işleme alandaki pek çok çalışma gibi, bu çalışmada da Evrişimsel Sinir Ağları (Convolutional Neural Network, CNN) mimarileri kullanılmıştır. AlexNet, GoogLeNet ve VGG-19 derin öğrenme modelleri ile öğrenme aktarımı gerçekleştirilmiş, destek vektör makineleri (Support Vector Machines, SVM) ile sınıflandırma performansı test edilmiştir.

show abstract

Section: Literatür çAlışmalarıunclassified

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Ergün¹,

Güney²,

Ergün³

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…Even though the results are quite high, this method only able to detect the position of the tumor and cannot recognize the image that contained the tumor or not. The combination of the feature extraction algorithm and CNN-SoftMax is proposed for detecting brain tumors [22]. This method claims to be able to obtain high accuracy results.…”

Section: Dataset and Related Workmentioning

confidence: 99%

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

Qodri

Soesanti

Nugroho

2021

IJITEE

View full text Add to dashboard Cite

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

show abstract

“…Deep learning (DL) models have demonstrated increasing utility within the field of oncology and particular promise in analyzing growing amounts of oncologic imaging [1,2]. DL models have been validated across a variety of diagnostic imaging modalities including MRI, CT, and X-ray images with classification accuracy often rivaling trained clinicians [3][4][5][6][7][8]. As widespread clinical implementation of DL models becomes a more realistic possibility, the safety of such models in healthcare is becoming a topic of increasing importance [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning (DL) algorithms have the promise to improve the quality of diagnostic image interpretation within oncology (1,2). Models generated from DL algorithms have been validated across a variety of diagnostic imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and X-ray images with classification accuracy often rivaling trained clinicians (3-9). However, the success of DL models depends, in part, on their generalizability and stability.…”

Section: Introductionmentioning

confidence: 99%

Using Adversarial Images to Assess the Stability of Deep Learning Models Trained on Diagnostic Images in Oncology

Joel

Umrao

Chang

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundDeep learning (DL) models have shown the ability to automate the classification of medical images used for cancer detection. Unfortunately, recent studies have found that DL models are vulnerable to adversarial attacks which manipulate models into making incorrect predictions with high confidence. There is a need for better understanding of how adversarial attacks impact the predictive ability of DL models in the medical image domain.MethodsWe studied the adversarial attack susceptibility of DL models for three common imaging tasks within oncology. We investigated how PGD adversarial training could be employed to increase model robustness against FGSM, PGD, and BIM attacks. Finally, we studied the utility of adversarial sensitivity as a metric to improve model performance.ResultsOur experiments showed that medical DL models were highly sensitive to adversarial attacks, as visually imperceptible degrees of perturbation (<0.004) were sufficient to deceive the model the majority of the time. DL models for medical images were more vulnerable to adversarial attacks compared to DL models for non-medical images. Adversarial training increased model performance on adversarial samples for all classification tasks. We were able to increase model accuracy on clean images for all datasets by excluding images most vulnerable to adversarial perturbation.ConclusionOur results indicated that while medical DL systems are extremely susceptible to adversarial attacks, adversarial training show promise as an effective defense against attacks. Adversarial susceptibility of individual images can be used to increase model performance by identifying images most at-risk for misclassification. Our findings provide a useful basis for designing more robust and accurate medical DL models as well as techniques to defend models from adversarial attack.

show abstract

Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm

Cited by 154 publications

References 14 publications

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

Using Adversarial Images to Assess the Stability of Deep Learning Models Trained on Diagnostic Images in Oncology

Contact Info

Product

Resources

About