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![[3(a)] H & E Stained Breast Malignant Tissue, [3(b)] Response of MCW, [3(c)] Nuclei Detected Output, [3(d)] VMCWM Nuclei Segmented Result, [3(e)] Multi-phase LS Result [3(f)] Proposed Integrated Nuclei Segmented Result](profile/Rajyalakshmi-Uppada/publication/315057959/figure/fig1/AS:738767677685761@1553147348666/3a-H-E-Stained-Breast-Malignant-Tissue-3b-Response-of-MCW-3c-Nuclei.png)
[3(a)] H & E Stained Breast Malignant Tissue, [3(b)] Response of MCW, [3(c)] Nuclei Detected Output, [3(d)] VMCWM Nuclei Segmented Result, [3(e)] Multi-phase LS Result [3(f)] Proposed Integrated Nuclei Segmented Result
Source publication
![Figure 3. [3(a)] H & E Stained Breast Malignant Tissue, [3(b)] Response...](profile/Rajyalakshmi-Uppada/publication/315057959/figure/fig1/AS:738767677685761@1553147348666/3a-H-E-Stained-Breast-Malignant-Tissue-3b-Response-of-MCW-3c-Nuclei_Q320.jpg)
Symptomatic awareness of the Breast Cancer (BC) in the early stage is needful for treatment and also to support the radiologists during their diagnosis. In the present module, nuclei detections of BC biopsy images stained with Hematoxylin and Eosin, are done using Hough Transform and their Segmentation with Proposed Variable - Marker Controlled Wat...
Contexts in source publication
Context 1
... the initial front for LS is provided by VMCWM. The integrated response of VMCWM and LS extracts clearly the nuclei shown in Figure 3. In multi phase LS a number of LS"s are initiated using VMCWM boundaries. ...
Context 2
... overlapped nuclei problem has solved in the proposed method as shown in Figure 3(f). ...
Context 3
... the initial front for LS is provided by VMCWM. The integrated response of VMCWM and LS extracts clearly the nuclei shown in Figure 3. In multi phase LS a number of LS"s are initiated using VMCWM boundaries. ...
Citations
The second biggest cause of mortality for women worldwide is Breast Cancer (BC). BC diagnosis by hand using histological breast pictures is expensive, time-consuming, and non-generalizable. Using a CNN to directly learn features from entire slide images is an alternative way for feature extraction. A significant number of labelled images, which can occasionally be challenging to get, are necessary for training the CNN. Reusing a pre-trained CNN model for feature attainment with huge image datasets from other disciplines is the solution. The BreakHis dataset contains images of BC histology, and in this article, we provide a “Novel CNN” architecture using Transfer Learning for identifying those images. This model's binary classification-benign and malignant-allows it to quickly and accurately diagnose breast cancer. In the suggested framework, DenseNet-201 pre-trained model is used to attain features from the histopathological pictures. Then, to generate a reliable hybrid model, the attained features are applied into the Global Average Pooling Layer, followed by Dropout, Batch-Normalization, and Dense Layers. The proposed model had a 99.75% accuracy rate. These encouraging findings will open the door to utilize this model as an automated tool to help clinicians diagnose breast cancer and may improve the survival rate for the disease.
Today, with the Evolution of technology and tools like computer systems, mobiles, tablets pieces use these http as default protocol. The fact is a huge amount of data transactions are being processed due to these the http protocol. Because of this reason http protocol is becoming a target to attackers. It is important to analyze traffic between various protocols to seek attack attempts and also take certain measures to prevent them from these attackers. In this we are going to evaluate the effectiveness of web application firewall by using Confusion matrix plots we use R studio as a tool for finding out the results. We focus on the Precision, Recall, Sensitivity, Accuracy and False Positive Rate with the help of random numbers. we used different classifiers to test the detection accuracies. The experiments show that we can also find effectiveness in a better way.
For women, breast cancer occupies the second position in causing the occurrence as well as mortality. Optimum segmentation and feature extraction play a crucial role while categorizing medical images. The proposed paper integrates marker-based watershed approach with K-means clustering data for optimum segmentation. It deals with detail component protection. The work focus on feature extraction from the segmented histopathological images. Feature selection is necessary for minimizing the redundant parameters. Optimum features necessary for image categorization were evaluated. The proposed work provides high accuracy during image classification.
