Physical and Engineering Sciences in Medicine

Aims & Scope

Physical and Engineering Sciences in Medicine is a multidisciplinary forum for information and research on the application of physics and engineering to medicine, covering a broad range of topics that include but is not limited to: - Radiation measurements and nuclear technology in medicine and physiology - Medical physics in nuclear medicine, radiotherapy, and diagnostic radiology - Clinical biomedical engineering - Signal processing and feature extraction of biomedical signals; - Medical imaging and image processing - contributions to new and improved methods; - Nanotechnology in medicine - Professional issues in medical physics and biomedical engineering Article types include reviews, scientific papers, notes, AI notes, book reviews and letters to the editor.

Physical and Engineering Sciences in Medicine is the journal of the Australasian College of Physical Scientists and Engineers in Medicine, and also the official journal of the College of Biomedical Engineers, Engineers Australia and the Asia-Oceania Federation of Organizations for Medical Physics.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2
2024	2.40

SJR (SCImago Journal Rank)

Year	Value
2024	0.482

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	51

Journal Rank

Year	Value
2024	11942

Journal Citation Indicator

Year	Value
2024	1030

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Biochemistry, Genetics and Molecular Biology, Engineering, Health Professions, Medicine and Physics and Astronomy, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

  Citation: 1718

  Authors: Ioannis D., Tzani A.

• Truncated inception net: COVID-19 outbreak screening using chest X-rays

  Citation: 216

  Authors: Dipayan, K. C., Umapada

• Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals

  Citation: 121

  Authors: Ahmad, Sara, Arash

• Lung and colon cancer classification using medical imaging: a feature engineering approach

  Citation: 109

  Authors: Aya, Nassib, Jean-Marie, Mohamad, Pierre

• Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors

  Citation: 106

  Authors: Muhammad, Furqan, Gulistan

• Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network

  Citation: 103

  Authors: Palani Thanaraj, Parvathavarthini, Snekhalatha

• Improved U-Net architecture with VGG-16 for brain tumor segmentation

  Citation: 101

  Authors: Sourodip, Aunkit, KC

• Diabetic retinopathy classification based on multipath CNN and machine learning classifiers

  Citation: 89

  Authors: S., Varun P., P.

• A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals

  Citation: 85

  Authors: Zülfikar, Mehmet

• Detection and analysis: driver state with electrocardiogram (ECG)

  Citation: 84

  Authors: Suganiya, Jerritta, Arun

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