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UID:20260522T105150EDT-8054Kh8TNj@132.216.98.100
DTSTAMP:20260522T145150Z
DESCRIPTION:Abstract\n\nSkin cancer is the fastest-growing form of cancer\,
  accounting for one-third of all cases worldwide and representing a major 
 public health threat. Visual assessment and invasive biopsy remain the cli
 nical standard\, but they depend on subjective perception and experience o
 f the dermatologist\, patient discomfort\, and lengthy waiting time for di
 agnostic results.\n\nMicrowave techniques show promise as a non-invasive a
 lternative by exploiting dielectric contrast between healthy skin and anom
 alous lesions. So far\, the reported sensors based on this approach have b
 een primarily reflectometry-based dielectric probes and antennas. These ap
 peared to struggle to provide\, within a single platform\, deployment flex
 ibility\, cost-effectiveness\, and quantitative dielectric characterizatio
 n. In contrast\, transmission- based approaches\, particularly those using
  surface waves\, show promising potential but remain largely unexplored.\n
 \nThe work of this thesis targets exploration of a surface-wave-based meth
 od to address these limitations in microwave-assisted skin cancer diagnosi
 s. In this approach\, two bio-compatible patch antennas are placed on oppo
 site sides of the suspicious skin region. One antenna excites and the othe
 r receives surface waves propagating along the skin–air interface. The cha
 llenge lies in strategic analysis of the transmitted response to uncover t
 he information about the dielectric profile\, thereby detecting the skin a
 nomaly.\n\nBuilding on this hardware concept\, a comprehensive\, end-to-en
 d frame-work is established. First\, a theoretical model describes how sur
 face waves interact with skin and lesions and links antenna phase shifts t
 o tissue permittivity. Subsequently\, the antenna evolution is shown\, inc
 luding one monopole-based design (Gen 1) and two Vivaldi-based designs (Ge
 n 2 and Gen 3). Next\, numerical simulation and phantom-based experiments 
 are presented to validate the theory and compare the performance of severa
 l antenna designs. The results confirm that the surface-wave method can ch
 aracterize permittivity contrast\, detect the presence of skin lesions\, a
 nd track their progression. A steady improvement in performance is observe
 d across successive antenna generations. Finally\, a deep-learning classif
 ier for skin-condition prediction is trained on measurements from 900 tiss
 ue models that reflect person-to-person variability. Using a convolutional
  neural network (CNN) enhanced by a bidirectional long short-term memory (
 Bi-LSTM) block\, the model achieves over 90% accuracy in flagging malignan
 t tumors\, demonstrating the potential of the surface-wave approach as a f
 lexible\, low-cost solution for skin cancer detection and monitoring.\n\nI
 n addition to the above-describe chief exploration\, two important issues 
 in microwave-based skin diagnostics are investigated. First\, the safety a
 nalysis is conducted via numerical tools\, to show that peak specific abso
 rption rate (SAR) can vary by more than 80% across different body sites\, 
 highlighting the need to account for tissue variability and providing a ba
 sis for a comprehensive evaluation framework. Second\, the work explores t
 he trade-off between the computational cost and complexity of tissue model
 s in numerical simulations\, using the results to provide a recommendation
  for optimized balance between computational economy and meaningfully accu
 rate simulation data.\n
DTSTART:20260521T140000Z
DTEND:20260521T160000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Shangyang Shang – Microwave Skin Spectroscopy for Ch
 aracterization and Lesion Diagnosis
URL:/ece/channels/event/phd-defence-shangyang-shang-mi
 crowave-skin-spectroscopy-characterization-and-lesion-diagnosis-372324
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