Meena L C | Anna University (original) (raw)

Papers by Meena L C

Research paper thumbnail of An optimal deep learning approach for breast cancer detection and classification with pretrained CNN-based feature learning mechanism

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as th... more Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage. Segmenting and classifying the tumors from ultrasound images is difficult. This paper proposes an optimal deep learning (DL)-based BC detection system with effective pre-trained transfer learning models-based segmentation and feature learning mechanisms. The proposed system comprises five phases: preprocessing, segmentation, feature learning, selection, and classification. Initially, the ultrasound images are collected from the breast ultrasound images (BUSI) dataset, and the preprocessing operations, such as noise removal using the Wiener filter and contrast enhancement using histogram equalization, are performed on the collected data to improve the dataset quality. Then, the segmentation of cancer-affected regions from the preprocessed data is done using a dilated convolution-based U-shaped network (DCUNet). The features are extracted or learned from the segmented images using spatial and channel attention including densely connected convolutional network-121 (SCADN-121). Afterwards, the system applies an enhanced cuckoo search optimization (ECSO) algorithm to select the features from the extracted feature set optimally. Finally, the ECSOtuned long short-term memory (ECSO-LSTM) was utilized to classify BC into '3' classes, such as normal, benign, and malignant. The experimental outcomes proved that the proposed system attains 99.86% accuracy for BC classification, which is superior to the existing state-of-the-art methods.

Research paper thumbnail of PFR Based Technique to Detect Intruder in MANET

Mobile Adhoc networks (MANET) are one category of wireless networks that operates without any cen... more Mobile Adhoc networks (MANET) are one category of wireless networks that operates without any centralized infrastructure. Every node in MANET not only acts as a host and also acts as a router to forward the packets from neighbor nodes. Security becomes a major concern in MANET. Intrusion detection system plays a major role to detect intruder in MANET. Here PFRACK approach is proposed to detect intruder in MANET. Here every node maintains PFR table which contains the packet forward ratio of the node. To access the performance, PFRACK is compared with A3ACK method in terms of packet delivery ratio and routing overhead. Results shows that PFRACK provides better packet delivery ratio and routing overhead compared to existing A3ACK method.

Research paper thumbnail of Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques

Brain tumor classification from MRI image is one of the important areas in biomedical research. H... more Brain tumor classification from MRI image is one of the important areas in biomedical research. However, the interpretation of these images is subjective, and automated detection methods can improve diagnostic accuracy. This study proposes a hybrid model that combines deep learning, machine learning, and traditional image processing techniques to detect brain tumors from MRI images. The proposed model is based on a convolutional neural network (CNN) algorithm and a machine learning model. The first stage is employing the histogram equalisation approach to enhance the image. In the second stage, features of the grey level co-occurrence matrix are extracted from the improved image. Convolutional neural networks are utilised in the third stage to categorise as normal or abnormal. The abnormal brain tumor is segmented using a morphological segmentation algorithm. This study suggests a number of performance criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed method. Our proposes algorithm demonstrates superior performance compared to existing methods regarding accuracy, sensitivity, and specificity.

Research paper thumbnail of Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging mod... more Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging modalities such as ultrasound. Ultrasound imaging is a widely used diagnostic modality for uterine fibroid due to its non-invasive nature. However, the images obtained often suffer from speckle noise, which can obscure fine details and complicate accurate diagnosis. Existing methods for removing speckle noise have limitations, including losing texture and edge information and not being able to handle low frequency noises. This paper presents a novel approach for speckle noise reduction by combining Shearlet Transform with Bayesian thresholding. The proposed method aims to achieve superior noise reduction while retaining important image features crucial for accurate diagnosis. Experimental results demonstrate the efficacy of the Shearlet Transform and Bayesian thresholding in significantly reducing speckle noise, enhancing image quality, and improving the interpretability of ultrasound images. Performance metrics like Mean Squared Error (MSE), Structural Similarity Index and Peak Signal to Noise Ratio (PSNR) helps to validate our proposed method. Reducing speckle noise in ultrasound images of uterine fibroids contributes to more accurate diagnosis and improves surgical treatment outcomes.

Research paper thumbnail of Detection of Breast Cancer using Curvelet Transform and Adaptive Particle Swarm Optimization Technique

The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is earl... more The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is early detection, which can also lower mortality rates. The best technique for early breast disease detection is the mammography. In the suggested approach, curvelet transform is utilized to extract features, and adaptive particle swarm optimization helps to choose the eminent features. Adaptive Particle Swarm Optimization has been devised to speed up and simplify the process of feature selection and Support Vector Machine (SVM) aids in breast cancer classification. We present an Adaptive Particle Swarm Optimization (APSO) that outperforms Particle Swarm Optimization (PSO) regarding search efficiency. The suggested model is examined using a collection of 332 images from the Mammographic Image Analysis Society (MIAS) database. The executed findings are compared with the old transforms, and the results demonstrate that the suggested model has higher detection accuracy rates than the earlier approaches.

Research paper thumbnail of Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images

Wiley, 2024

Non-cancerous growths called uterine fibroids develop in the uterus. They can vary in size, locat... more Non-cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely interven

Research paper thumbnail of An optimal deep learning approach for breast cancer detection and classification with pretrained CNN-based feature learning mechanism

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as th... more Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage. Segmenting and classifying the tumors from ultrasound images is difficult. This paper proposes an optimal deep learning (DL)-based BC detection system with effective pre-trained transfer learning models-based segmentation and feature learning mechanisms. The proposed system comprises five phases: preprocessing, segmentation, feature learning, selection, and classification. Initially, the ultrasound images are collected from the breast ultrasound images (BUSI) dataset, and the preprocessing operations, such as noise removal using the Wiener filter and contrast enhancement using histogram equalization, are performed on the collected data to improve the dataset quality. Then, the segmentation of cancer-affected regions from the preprocessed data is done using a dilated convolution-based U-shaped network (DCUNet). The features are extracted or learned from the segmented images using spatial and channel attention including densely connected convolutional network-121 (SCADN-121). Afterwards, the system applies an enhanced cuckoo search optimization (ECSO) algorithm to select the features from the extracted feature set optimally. Finally, the ECSOtuned long short-term memory (ECSO-LSTM) was utilized to classify BC into '3' classes, such as normal, benign, and malignant. The experimental outcomes proved that the proposed system attains 99.86% accuracy for BC classification, which is superior to the existing state-of-the-art methods.

Research paper thumbnail of PFR Based Technique to Detect Intruder in MANET

Mobile Adhoc networks (MANET) are one category of wireless networks that operates without any cen... more Mobile Adhoc networks (MANET) are one category of wireless networks that operates without any centralized infrastructure. Every node in MANET not only acts as a host and also acts as a router to forward the packets from neighbor nodes. Security becomes a major concern in MANET. Intrusion detection system plays a major role to detect intruder in MANET. Here PFRACK approach is proposed to detect intruder in MANET. Here every node maintains PFR table which contains the packet forward ratio of the node. To access the performance, PFRACK is compared with A3ACK method in terms of packet delivery ratio and routing overhead. Results shows that PFRACK provides better packet delivery ratio and routing overhead compared to existing A3ACK method.

Research paper thumbnail of Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques

Brain tumor classification from MRI image is one of the important areas in biomedical research. H... more Brain tumor classification from MRI image is one of the important areas in biomedical research. However, the interpretation of these images is subjective, and automated detection methods can improve diagnostic accuracy. This study proposes a hybrid model that combines deep learning, machine learning, and traditional image processing techniques to detect brain tumors from MRI images. The proposed model is based on a convolutional neural network (CNN) algorithm and a machine learning model. The first stage is employing the histogram equalisation approach to enhance the image. In the second stage, features of the grey level co-occurrence matrix are extracted from the improved image. Convolutional neural networks are utilised in the third stage to categorise as normal or abnormal. The abnormal brain tumor is segmented using a morphological segmentation algorithm. This study suggests a number of performance criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed method. Our proposes algorithm demonstrates superior performance compared to existing methods regarding accuracy, sensitivity, and specificity.

Research paper thumbnail of Innovative Noise Reduction Strategies in Ultrasound Images Using Shearlet Transform and Bayesian Thresholding

Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging mod... more Uterine fibroids are prevalent benign tumors affecting women, often diagnosed through imaging modalities such as ultrasound. Ultrasound imaging is a widely used diagnostic modality for uterine fibroid due to its non-invasive nature. However, the images obtained often suffer from speckle noise, which can obscure fine details and complicate accurate diagnosis. Existing methods for removing speckle noise have limitations, including losing texture and edge information and not being able to handle low frequency noises. This paper presents a novel approach for speckle noise reduction by combining Shearlet Transform with Bayesian thresholding. The proposed method aims to achieve superior noise reduction while retaining important image features crucial for accurate diagnosis. Experimental results demonstrate the efficacy of the Shearlet Transform and Bayesian thresholding in significantly reducing speckle noise, enhancing image quality, and improving the interpretability of ultrasound images. Performance metrics like Mean Squared Error (MSE), Structural Similarity Index and Peak Signal to Noise Ratio (PSNR) helps to validate our proposed method. Reducing speckle noise in ultrasound images of uterine fibroids contributes to more accurate diagnosis and improves surgical treatment outcomes.

Research paper thumbnail of Detection of Breast Cancer using Curvelet Transform and Adaptive Particle Swarm Optimization Technique

The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is earl... more The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is early detection, which can also lower mortality rates. The best technique for early breast disease detection is the mammography. In the suggested approach, curvelet transform is utilized to extract features, and adaptive particle swarm optimization helps to choose the eminent features. Adaptive Particle Swarm Optimization has been devised to speed up and simplify the process of feature selection and Support Vector Machine (SVM) aids in breast cancer classification. We present an Adaptive Particle Swarm Optimization (APSO) that outperforms Particle Swarm Optimization (PSO) regarding search efficiency. The suggested model is examined using a collection of 332 images from the Mammographic Image Analysis Society (MIAS) database. The executed findings are compared with the old transforms, and the results demonstrate that the suggested model has higher detection accuracy rates than the earlier approaches.

Research paper thumbnail of Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images

Wiley, 2024

Non-cancerous growths called uterine fibroids develop in the uterus. They can vary in size, locat... more Non-cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely interven