Pulmonary Nodule Detection Using Laplacian of Gaussian and Deep Convolutional Neural Network

Bhaskar, Nuthanakanti; Ganashree, T S

doi:10.1007/978-981-16-9669-5_58

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Furthermore, multilevel contextual information encoded by the adaptive conv-kernels method improved nodule classification accuracy. Bhaskar and Ganashree [44] introduced an effective method using multi-scale Laplacian of Gaussian filters and deep convolutional neural network to detect pulmonary nodules and achieved 71.2% recall, and 93.2% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

et al. 2023

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Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

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Section: Literature Reviewmentioning

confidence: 99%

“…Table 1 is showing some limitations of the previous studies included hand crafted features [39], [41], [46], the need to improve accuracy [35], [40], [44], [47], [48], [51] lack of transparency [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

et al. 2023

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“…Tiwari et al [5] investigated the reliability of a deep learning system for diagnosing lung disease based on clinical image analysis challenges. Using multiscale Laplacian of Gaussian filters, dimensions, and structure restrictions, Bhaskar and Ganashree [6] identified nodule candidates.…”

Section: Introductionmentioning

confidence: 99%

An approach for liver cancer detection from histopathology images using hybrid pre-trained models

Bhaskar,

Kiran,

Satyanarayan

et al. 2024

TELKOMNIKA

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Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for imagelevel classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread.

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An Image Processing Approach for Weed Detection Using Deep Convolutional Neural Network

Aparna,

Bhaskar,

Srujan Raju

et al. 2024

Lecture Notes in Networks and Systems

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Pulmonary Nodule Detection Using Laplacian of Gaussian and Deep Convolutional Neural Network

Cited by 6 publications

References 29 publications

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

An approach for liver cancer detection from histopathology images using hybrid pre-trained models

An Image Processing Approach for Weed Detection Using Deep Convolutional Neural Network

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