Pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images

Thanupillai, Kalai Selvi; Basha, Rahimunnisa Kamal

doi:10.1002/cpe.7448

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…The main advantage of the PCNN method is that no training process is required to apply image fusion. The main advantage of the PCNN approach is that image fusion can be applied without a training process [38][39][40]. PCNNs are represented as single-layer networks involving multiple neuron connections, which inevitably leads to computational complexity, low time efficiency and poor fusion accuracy.…”

Section: High Frequency Fusionmentioning

confidence: 99%

An image fusion-based method for recovering the 3D shape of roll surface defects

Xu,

Lou

et al. 2023

Meas. Sci. Technol.

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Most of the existing studies on roll surface defects focus on qualitative detection and lack of quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are stylus method, laser scanning method and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform (NSST) is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called Mm-PCNN to obtain a fully focused image. The method achieves 3D shape recovery of defects by modelling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. The experimental results show that the proposed method effectively integrates detailed information and achieves a reconstruction error control of less than 4%, thus ensuring higher accuracy and improved noise immunity.

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Section: High Frequency Fusionmentioning

confidence: 99%

An image fusion-based method for recovering the 3D shape of roll surface defects

Xu,

Lou

et al. 2023

Meas. Sci. Technol.

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“…Expert ultrasound examination is a vital imaging technique for examining gynecological abdominal pelvic masses [12][13][14]. Although there is a dearth of expertise, ultrasound has higher diagnostic accuracy in the hands of experts than in those of less experienced medical professionals [15][16][17][18]. Amongst the various research works on pelvic mass classification, some of the latest investigations are assessed here.…”

Section: Introductionmentioning

confidence: 99%

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer

Deeparani,

Kalamani

2023

Diagnostics

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Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

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Pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images

Cited by 2 publications

References 30 publications

An image fusion-based method for recovering the 3D shape of roll surface defects

An image fusion-based method for recovering the 3D shape of roll surface defects

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer

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