Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images

Qiu, Yuchen; Thai, Theresa C.; Moore, Kathleen N.; Liu, Hong

doi:10.1117/12.2250360

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Langkah pra-pemrosesan gambar termasuk algoritma segmentasi batang tubuh dan proses ambang batas pertama kali dilakukan untuk mengidentifikasi piksel jaringan adiposa yang termasuk dalam area lemak; Segmentasi-CNN kemudian diterapkan untuk mengklasifikasikan setiap jaringan adiposa tunggal sebagai milik SFA atau VFA.(Y. Wang et al, 2017b) Untuk pendekatan CNN segmentasi ini penulis mengikuti pendekatan Yunzhi wang dan tim mereka menggunakan kombinasi lapisan CONV2D, Maxpooling dan Upsampling. Tapi penulis awalnya mendapatkan kesalahan bentuk dengan Conv2D jadi penulis melanjutkan dengan Conv1D.…”

Section: Gambar Ambang Batasunclassified

Segmentasi Lemak Visceral dan Kanker Paru-paru Menggunakan Deep Learning

et al. 2022

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Lemak Visceral memberikan dampak yang sangat besar terhadap beberapa penyakit dan salah satunya Kanker paru-paru penyebab utama kematian, Kondisi ini memberikan pengaruh besar dalam dunia kesehatan terhadap permasalahan Lemak visceral dan kanker paru-paru Computer vision menghembalangkan penelitian dari permasalahan yang besar ini computer vision ingin melihat keakuratan penelitian, dengan dataset yang telah di tentukan dengan ini peneliti menggunakan metode deep learning dan di dukung dengan metode Segmentasi-CNN, Segmentation & VFI Calculation. Metode tersebut mampu menampilkan gambar yang tersegmentasi dengan detail dari mengubah warna satu dimensi dan dua dimensi sehingga akan memberikan keakuratan segmentasi, lemak yang telah tersegmentasi daerah gambar lemak dan garis tepinya pada Lemak visceral dan kanker paru-paru memisahkan lemak dan kanker dengan jumlah pixel putih lemak lemah dengan ration area akurat dan Memiliki pixel yang tinggi dan tepat dalam pemrosesan pixel.

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Section: Gambar Ambang Batasunclassified

Segmentasi Lemak Visceral dan Kanker Paru-paru Menggunakan Deep Learning

et al. 2022

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Lightweight convolutional neural network (CNN) model for obesity early detection using thermal images

Leo,

Saddami,

Roslidar

et al. 2024

DIGITAL HEALTH

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Objective The presence of a lightweight convolutional neural network (CNN) model with a high-accuracy rate and low complexity can be useful in building an early obesity detection system, especially on mobile-based applications. The previous works of the CNN model for obesity detection were focused on the accuracy performances without considering the complexity size. In this study, we aim to build a new lightweight CNN model that can accurately classify normal and obese thermograms with low complexity sizes. Methods The DenseNet201 CNN architectures were modified by replacing the standard convolution layers with multiple depthwise and pointwise convolution layers from the MobileNet architectures. Then, the depth network of the dense block was reduced to determine which depths were the most comparable to obtain minimum validation losses. The proposed model then was compared with state-of-the-art DenseNet and MobileNet CNN models in terms of classification performances, and complexity size, which is measured in model size and computation cost. Results The results of the testing experiment show that the proposed model has achieved an accuracy of 81.54% with a model size of 1.44 megabyte (MB). This accuracy was comparable to that of DenseNet, which was 83.08%. However, DenseNet’s model size was 71.77 MB. On the other hand, the proposed model’s accuracy was higher than that of MobileNetV2, which was 79.23%, with a computation cost of 0.69 billion floating-point operations per second (GFLOPS), which approximated that of MobileNetV2, which was 0.59 GFLOPS. Conclusions The proposed model inherited the feature-extracting ability from the DenseNet201 architecture while keeping the lightweight complexity characteristic of the MobileNet architecture.

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Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images

Cited by 2 publications

References 16 publications

Segmentasi Lemak Visceral dan Kanker Paru-paru Menggunakan Deep Learning

Segmentasi Lemak Visceral dan Kanker Paru-paru Menggunakan Deep Learning

Lightweight convolutional neural network (CNN) model for obesity early detection using thermal images

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