| 3 | Proposed Framework for Detection of Breast Tumors显示文摘Computer vision is one of the significant trends in computer science.It plays as a vital role in many applications,especially in the medical field.Early detection and segmentation of different tumors is a big challenge in the medical world.The proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or normal.The main challenge faced by the framework in the segmentation is the speckle noise.It’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging modality.As result,speckle noise reduction is very vital for the segmentation process.The framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of speckle-noise.The five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of noise.The five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF respectively.According to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor type.The experiments proved the quality ofU-Net and CNN in segmentation and classification respectively,since they achieved 95.11 and 95.13 in segmentation and 95.55 and 95.67 in classification as dice score and accuracy respectively. | Mostafa Elbaz Haitham Elwahsh Ibrahim Mahmoud El-Henawy | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 4 | Novel Framework of Segmentation 3D MRI of Brain Tumors显示文摘Medical image segmentation is a crucial process for computer-aided diagnosis and surgery.Medical image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and examination.Rician and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process negatively.Therefore,image enhancement has a significant role in MRI segmentation.This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.The proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor.The framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction stage.The framework presents solutions for each problem at a different stage of the segmentation.In the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the Rician.Afterwards,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle noise.VMDand Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)respectively.The framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,respectively.The experiments that have been conducted have proved the efficiency of the proposed framework against classical filters such as Bilateral,Frost,Kuan,and Lee according to different levels of noise.The attention gate U-Net achieved 94.66 and 95.03 in the segmentation of free noise images in dice and accuracy,respectively. | Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr | 2023 | Computers, Materials & Continua2023，，2： | 0 |
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