Enhanced Image Compression Using Fractals and Principle Component Analysis

Tiwari, Manish; Chaurasia, Vijayshri; Siddiqui, Ebtasam Ahmad; Patel, Vivek; Kumar, Akhilesh; Patankar, Mamta

doi:10.1109/ihcsp56702.2023.10127168

Cited by 2 publications

(5 citation statements)

References 23 publications

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“…The identification of cell nuclei for diagnostic reasons is a crucial component of many laboratory tests used in medicine [5,7,10,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Correct diagnosis and automated microscopy applications, such as cell counting and tissue architecture analysis, can both benefit from the precise placement of cell nuclei [12].…”

Section: Breast Cancer Diagnosis Methodsmentioning

confidence: 99%

“…The nuclei are crucial in and of themselves for assessing and verifying the presence of illness and the speed at which it spreads. A few of the feature detection techniques that have been employed successfully and efficiently are discussed in this study [33][34][35][36][37][38][39][40][41][42][43].…”

Section: Detection Of Featuresmentioning

confidence: 99%

“…, 𝑤 𝐿 ) = 𝑓 𝐿 (𝑤 𝐿 )° 𝑓 𝐿−1 (𝑤 𝐿−1 )°… . °𝑓2 (𝑤 2 )°𝑓 1 (𝑤 1 ) The convolutional layer is a layer that multiplies pixels one by one using the kernel coefficients and the total of all the window's pixel values [35,36,42]. DCNN employs n x n unpadded convolutions with the specified kernel applied repeatedly, each convolution being followed by a rectified linear unit (ReLU) and a max pooling operation with a specific downsampling stride.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…In order to make the network deeper, as illustrated in figure (4), the whole unit (convolution+ReLU+Max Pooling) is concatenated with the proportionally cropped feature map from the contracting, and the necessary number of convolutions with a certain kernel, followed by a ReLU. The loss of boundary pixels in each convolution necessitates cropping [26][27][28][29][30][31][32][33][34][35].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…Now, we may get the categorized picture using the soft-max algorithm. Other existing neural networks are in use, like ResNet and U-Net, which are used specifically for segmenting biological images [30][31][32][33][34][35][36][37].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

See 4 more Smart Citations

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Patel,

Chaurasia,

Mahadeva

et al. 2023

E3S Web Conf.

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Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms.

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Section: Breast Cancer Diagnosis Methodsmentioning

confidence: 99%

Section: Detection Of Featuresmentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Patel,

Chaurasia,

Mahadeva

et al. 2023

E3S Web Conf.

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Fractal and Multifractal Characterization of 3D Video Signals

Zekovic

2023

Fractal Analysis - Applications and Updates

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Video signals are responsible for the largest amount of information in data storage and data transmission systems. Three-dimensional (3D) video formats are the largest in terms of the amount of data, and thus the required bit rate. For the efficient transmission of 3D video in communication systems, a detailed knowledge of the traffic characteristics of the format is necessary. In this research, characterization of 3D video signals is performed by using fractal and multifractal analyses. Codes for analyses are written in MATLAB and Python. Communication network traffic shows self-similar behavior with long-range dependence (LRD). Using visual methods and a rigorous statistical methods, it was shown that video sequences of 3D video formats have fractal self-similar properties with LRD and high Hurst parameter values. It is shown that investigated 3D video formats have multifractal structure by using the histogram method. The research included different 3D video formats, specifically with one video sequence (frame compatible, FC and frame sequential, FS) and with two or more video sequences (multiview, MV). Cases with different signal qualities defined by the quantization parameter, different types of frames, different groups of picture (GoP), and different broadcasting methods were taken into account and separately analyzed.

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Enhanced Image Compression Using Fractals and Principle Component Analysis

Cited by 2 publications

References 23 publications

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Fractal and Multifractal Characterization of 3D Video Signals

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