Detection and Classification of Breast Cancer from Microscopic Biopsy Images using Modified Neural Network

Vanitha, L.; Jayamani, K.; Sasirekha, N.; Yogalakshmi., V; Sajiv, G

doi:10.1109/icacrs55517.2022.10029134

Cited by 4 publications

(3 citation statements)

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“…The RF binary classifier portioned the abdominal CT scanned images into the liver and tumor layer. The training and testing were done using the Gabor-based feature dataset (L. Zeng et al, 2016). By using the training and testing data, the input abdominal CT image is labeled corresponding to the 26 features of Gabor feature dataset as shown in Figure 4.…”

Section: Execution Of Rf Classifier For Liver Tumor Segmentationmentioning

confidence: 99%

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Automatic liver tumor segmentation from CT images using random forest algorithm

Sasirekha,

Anitha,

et al. 2023

The Scientific Temper

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Automatic liver segmentation is challenging, and the tumor segmenting process adds more complexity. Based on the grey levels and shape, separating the liver and tumor from abdominal CT images is critical. In our paper suggests a more effective approach by using Gabor features (GF) to segment liver tumors from CT images and three alternative neural network algorithms to address these problems: RF, CNN and ANN. This thesis uses the same collection of classifiers and GF to first segment a variety of Gabor liver images. The organ (liver) is then extracted from an abdominal CT image using liver segmentation, which is done by three classifiers: ANN, CNN, RF trained on Gabor filter and the tumor segmentation is done by the human visual system (HVS). For pixel-wise segmentation, reliable and accurate ML techniques were used. For the liver segmentation, the classification accuracy was 99.55, 97.88 and 98.13% for RF, CNN and ANN, respectively. From the extracted image of liver, the classification accuracy for tumor was 99.52, 98.07 and 98.45% for RF, CNN and ANN, respectively.

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Section: Execution Of Rf Classifier For Liver Tumor Segmentationmentioning

confidence: 99%

“…First, in images from an abdominal CT scan, the liver was separated from the other organs. The Gabor characteristics of the CT images trained by the ML algorithms (RF, ANN and Vanitha et al, 2022). The trained and then tested data were separated with the purpose of segmenting the liver.…”

Section: Liver Segmentationmentioning

confidence: 99%

Automatic liver tumor segmentation from CT images using random forest algorithm

Sasirekha,

Anitha,

et al. 2023

The Scientific Temper

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“…For this reason, the greater the breast density, the lower the sensitivity and specificity of mammography. Additionally, increased breast density is associated, at the time of diagnosis, with increased size of lesions and a worse prognosis (Boyd et al, (2005), Ramya et al (2015), Vanitha et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Classification of mammograms by breast density

Sasirekha,

Karuppaiah,

Thangavel

et al. 2023

The Scientific Temper

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The risk of getting breast cancer is directly affected by the type of breast tissue predominant in the individual. The aim is to investigatehistogram-based image attributes in order to separate mammographic images by degree of breast density using the clustering technique. 75 mammographic images from the MIAS database were used, 25 of them belonging to each of the three classes: fatty, fatty-glandular and dense. After the selection of attributes, it obtained a 96% success rate in classifying the mammograms within the three classes when the attribute’s mean gray levels and the highest peak intensity of the histogram were used simultaneously in the clustering technique

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