Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Nazir, Imran; Haq, İhsan Ul; Khan, Muhammad Ahsan; Qureshi, Muhammad Bilal; Ullah, Hayat; Butt, Sharjeel

doi:10.3390/electronics11010034

Cited by 17 publications

(20 citation statements)

References 52 publications

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“…The MATLAB Image Processing Toolbox was utilized to prototype the methods for image processing in this research. Grayscale conversion, adaptive thresholding segmentation, and morphological operations were all part of the image processing procedures were carried out systematically for 4000 images 5 , 76 – 78 . are grayscale images of the pearl chains in a Euclidean space .…”

Section: Methodsmentioning

confidence: 99%

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Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Ajala

Jalajamony

Nair

et al. 2022

Sci Rep

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An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.

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Section: Methodsmentioning

confidence: 99%

“…In the next steps, a morphological operation called dilation is applied on using structuring element (an array of horizontal and vertical lines). The dilation of by is mathematically defined as in 76 , 77 by Eq. ( 8 ) and ( 9 ) below, where is the translation of the array by the vector and is a null set: …”

Section: Methodsmentioning

confidence: 99%

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Ajala

Jalajamony

Nair

et al. 2022

Sci Rep

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“…MLbased tissue and tumor segmentation models have been widely proposed as auxiliary diagnostic tools. Two examples of the recent work on segmentation are Nazir et al [13] and Nishio et al [14] . In Nazir et al [13] approach , adaptive global threshold is applied for lung segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Two examples of the recent work on segmentation are Nazir et al [13] and Nishio et al [14] . In Nazir et al [13] approach , adaptive global threshold is applied for lung segmentation. The threshold is elected based on CT image histogram.…”

Section: Related Workmentioning

confidence: 99%

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

2022

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Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.

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“…Different types of medical images contain different information, and the obtained fused image can summarize various feature information to provide medical staff with more comprehensive pathological information ( Zhu et al, 2017 ). Common medical images include CT images, MR images, and SPECT images ( Thieme et al, 2012 ; Nazir et al, 2021 ; Engudar et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

Fang

Wang

et al. 2022

Front. Genet.

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Multi-modal medical image fusion can reduce information redundancy, increase the understandability of images and provide medical staff with more detailed pathological information. However, most of traditional methods usually treat the channels of multi-modal medical images as three independent grayscale images which ignore the correlation between the color channels and lead to color distortion, attenuation and other bad effects in the reconstructed image. In this paper, we propose a multi-modal medical image fusion algorithm with geometric algebra based sparse representation (GA-SR). Firstly, the multi-modal medical image is represented as a multi-vector, and the GA-SR model is introduced for multi-modal medical image fusion to avoid losing the correlation of channels. Secondly, the orthogonal matching pursuit algorithm based on geometric algebra (GAOMP) is introduced to obtain the sparse coefficient matrix. The K-means clustering singular value decomposition algorithm based on geometric algebra (K-GASVD) is introduced to obtain the geometric algebra dictionary, and update the sparse coefficient matrix and dictionary. Finally, we obtain the fused image by linear combination of the geometric algebra dictionary and the coefficient matrix. The experimental results demonstrate that the proposed algorithm outperforms existing methods in subjective and objective quality evaluation, and shows its effectiveness for multi-modal medical image fusion.

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Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Cited by 17 publications

References 52 publications

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

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