A Review of Deep Learning Approaches for Image Analysis

Aslam, Yasir; Santhi, N.

doi:10.1109/icssit46314.2019.8987922

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…Image analysis using deep learning methods is a rapidly growing field with many algorithms competing over a wide variety of applications (e.g. LSTM and RCNN) [35]. For medical image classification, Convolutional Neural Networks (CNN) are the most popular [36].…”

Section: Step 3: Model Architecture and Trainingmentioning

confidence: 99%

Deep learning pipeline for image classification on mobile phones

Muneeb,

Feng,

Henschel

2022

Preprint

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This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone and requires a systematic approach to find a model that performs optimally on both computers and mobile phones. By following the proposed pipeline, which consists of various computational tools, simple procedural recipes, and technical considerations, one can bring the power of deep learning medical image classification to mobile devices, potentially unlocking new domains of applications. The pipeline is demonstrated on four different publicly available datasets: COVID X-rays, COVID CT scans, leaves, and colorectal cancer. We used two application development frameworks: TensorFlow Lite (real-time testing) and Flutter (digital image testing) to test the proposed pipeline. We found that transferring deep learning models to a mobile phone is limited by hardware and classification accuracy drops. To address this issue, we proposed this pipeline to find an optimized model for mobile phones. Finally, we discuss additional applications and computational concerns related to deploying deep-learning models on phones, including real-time analysis and image preprocessing. We believe the associated documentation and code can help physicians and medical experts develop medical image classification applications for distribution.

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Section: Step 3: Model Architecture and Trainingmentioning

confidence: 99%

Deep learning pipeline for image classification on mobile phones

Muneeb,

Feng,

Henschel

2022

Preprint

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“…Deep learning (DL) is a branch of machine learning (ML), and refers to data driven modelling techniques, which applies the principles of simplified neuron interactions [ 9 ]. The application of imaging analysis techniques using artificial neurons on medical imaging started to draw attention decades ago [ 10 ], but it only became a major research focus recently due to the advancement in computational capacities and imaging techniques [ 11 , 12 ]. The artificial neuron model is used as a foundation unit to create complex chains of interactions - DL layers.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci

et al. 2023

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Purpose Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans. Methods We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians. Results The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.

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“…18,30,31 However, to reduce subjectivity and increase efficiency, deep learning techniques have achieved substantial interest in areas such as image segmentation, analysis, and classification. 32,33 Convolutional neural network (CNN) has achieved great success in several tasks within areas like biomedical image analysis and computer vision. 32,34 A prominent example of deep neural networks within marine biofouling is CoralNet.…”

Section: Introductionmentioning

confidence: 99%

Marine biofouling resistance rating using image analysis

et al. 2022

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A Review of Deep Learning Approaches for Image Analysis

Cited by 10 publications

References 30 publications

Deep learning pipeline for image classification on mobile phones

Deep learning pipeline for image classification on mobile phones

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci

Marine biofouling resistance rating using image analysis

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