Watershed Algorithm based DAPP features for Brain Tumor Segmentation and Classification

Jemimma, T. A.; Vetharaj, Y. Jacob

doi:10.1109/icssit.2018.8748436

Cited by 42 publications

(12 citation statements)

References 12 publications

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“…Although the method produces low computation, the accuracy that is obtained using these methods is not explained. A study explains that the Watershed Dynamic Angle Projection -Convolution Neural Network (WDAPP-CNN) method is used for the segmentation and classification of brain tumors [16]. The proposed algorithm gets high enough results for sensitivity and efficiency.…”

Section: Dataset and Related Workmentioning

confidence: 99%

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

Qodri

Soesanti

Nugroho

2021

IJITEE

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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.

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Section: Dataset and Related Workmentioning

confidence: 99%

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

Qodri

Soesanti

Nugroho

2021

IJITEE

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“…The final post process used is the Top-hat filter for reducing the brightness of the image. Jemimma and Vetharaj (2018) focus on Dynamic Angle Projection Pattern (DAPP) as a post-processing method after watershed segmentation. This method uses a 5×5 mask where the pixel value at the middle is rotated 45° with respect to the neighboring pixels.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance evaluation of brain tumor detection using watershed Segmentation and thresholding

Mishra

Roy

Bapat

et al. 2021

International Journal on Smart Sensing and Intelligent Systems

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Brain tumors and cancers are life-threatening diseases to human beings and have been on the rise. If undetected, they are deadly. With the advent of advanced medical technology, it has become imperative to accurately spot and identify these tumors at the earliest. The manuscript aims at providing an accurate method to detect and segment brain tumors from MRI scans. This is achieved by implementing watershed segmentation and threshold algorithm paired with pre and post image processing techniques. Apart from detecting the tumor region, the proposed process also enhances image quality by noise removal techniques and image quality improvement. These results give promising values when verified using several evaluation parameters such as Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM) and Peak Signal-to-Noise Ratio (PSNR) and stand out among the other similar pre-existing algorithms that they are compared with in a comparative analysis.

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“…For a variety of clinical studies of varying complexity, MRI segmentation and classification have been suggested. Medical image processing is usually considered a clinical analysis or a radiology-based analysis and the medical practitioner/radiologist is responsible for the interpretation of the imaging in clinical circumstances [10]. The technical aspect of medical imaging and in particular the acquisition of medical images by a special device is assigned by the diagnostic radiograph or the medical practitioner.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Images Using Deep Convolutional Autoencoders for Brain Tumor Classification

Revathi¹,

Muddana²

2021

RIA

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In the acquisition of images of the human body, medical imaging devices are crucial. The Magnetic Resonance Imaging (MRI) system detects tissue anomalies and tumours in the body of people. During the forming process, the MRI images are degraded by different kind of noises. It is difficult to remove certain noises, accompanied by the segmentation of images in order to classify anomalies. The most commonly explored areas of this period are automatic tumour detection systems using Magnetic Resonance Imaging. In the medical sector, timely and exact identification of frequencies is a problem. Automated systems are efficient that reduce human errors when tumour is detected. In recent years, many approaches have been proposed to do this, but there are still several drawbacks and a wide range of improvements on these methodologies are still needed. The image processing mechanism is widely used to improve early detection and treatment stages in the field of medical sciences. Sometimes the doctor can misdiagnose the image of MRI because of noise levels. To date, Deep Convolution Neural Networks (DCNN) have demonstrated excellent classification and segmentation efficiency. This paper proposes a technique for the image denoising using DCNN based Auto Encoders (DCNNAE) for achieving better accuracy rates in brain tumour prediction. In this paper we propose a deep convolution denoising auto encoder to remove noise from images and over fit the model problem by developing a deep convolution neural network for brain MRI image tumour prediction. The proposed model is compared with the existing methods and the results exhibits that the proposed model performance levels are better than the existing ones.

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Watershed Algorithm based DAPP features for Brain Tumor Segmentation and Classification

Cited by 42 publications

References 12 publications

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

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

Performance evaluation of brain tumor detection using watershed Segmentation and thresholding

Denoising of Images Using Deep Convolutional Autoencoders for Brain Tumor Classification

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