A Novel Texture Extraction‑Based Compressive Sensing for Lung Cancer Classification
Publish place: Journal of medical signals and sensors، Vol: 12، Issue: 4
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMSI-12-4_002
تاریخ نمایه سازی: 28 تیر 1402
Abstract:
Background: Lung cancer images require large memory storage and transmission bandwidth for
sending the data. Compressive sensing (CS), as a method with a statistical approach in signal
sampling, provides different output patterns based on information sources. Thus, it can be considered
that CS can be used for feature extraction of compressed information. Methods: In this study, we
proposed a novel texture extraction‑based CS for lung cancer classification. We classify three types
of lung cancer, including adenocarcinoma (ACA), squamous cell carcinoma (SCC), and benign lung
cancer (N). The classification is carried out based on texture extraction, which is processed in ۲
stages, the first stage to detect N and the second to detect ACA and SCC. Results: The simulation
results show that two‑stage texture extraction can improve accuracy by an average of ۸۴%. The
proposed system is expected to be decision support in assisting clinical diagnosis. In terms of
technical storage, this system can save memory resources. Conclusions: The proposed two‑step
texture extraction system combined with CS and K‑ Nearest Neighbor has succeeded in classifying
lung cancer with high accuracy; the system can also save memory storage. It is necessary to examine
the complexity of the proposed method so that it can be analyzed further.
Keywords:
Authors
Indrarini Dyah Iravati
School of Applied Science, Telkom University
Sugondo Hadiyoso
School of Applied Science, Telkom University
Gelar Budiman
School of Electrical Engineering, Telkom University, Bandung, Jawa Barat, Indonesia
Arfianto Fahmi
School of Electrical Engineering, Telkom University, Bandung, Jawa Barat, Indonesia