Various Image Modalities Used in Computer-Aided Diagnosis System for Detection of Breast Cancer Using Machine Learning Techniques: A Systematic Review

Wadhwa, Gitanjali; Kaur, Amandeep

doi:10.1007/978-981-16-1249-7_27

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Before the advent of the modern image modality capturing systems, the surgeons mostly relied on simple cameras and naked eyes to study the internal behaviour of the organs. Today, the most relied imaging modalities include X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), and Positron Emission Tomography (PET) [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and Long Short Term Memory (LSTM) networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

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Section: Introductionmentioning

confidence: 99%