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SEGMENTASI DAN KLASIFIKASI PENYAKIT PARU-PARU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Various kinds of lung diseases have become the main cause of death and the cause of many respiratory system complications that result in the death of millions of people every year. The use of medical images along with the deep learning methods can provide faster and more accurate detection of the disease. However, detection using medical images of the lungs also has challenges in the form of variations in the shape, size, and location of the infection area. In addition, multiple lung diseases often have similar symptoms. This study adopted a convolutional neural network (CNN) method with Mask-RCNN architecture to classify multi-class lung diseases. Eight models were employed with different configurations and the best performance was shown by the Mask-RCNN Resnet-50 model, learning rate 10-3, and learning cycle of 100 epochs. The evaluation matrix used shows the results of DSC, MIoU, Precision, recall, and accuracy of 91.98%, 85.25%, 98.84%, 98.86%, 99.47%, respectively. Testing of unseen data is also carried out to test the robustness of the model that has been built.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003685	T78615	T786152022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T786152022

Publisher

Inderalaya : Prodi Magister Ilmu Komputer, Fakultas Ilmu Komputer., 2022

Collation

xiv, 59 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jaringan Komunikasi Komputer
Prodi Magister Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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