Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images

Sundararajan, Senthil Kumar; Sankaragomathi, B.; Priya, Dr.S.Manju

doi:10.1007/s10916-019-1305-6

Cited by 45 publications

(17 citation statements)

References 21 publications

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“…, u l−1 , u l = j} that is the subset of feature pairs over the path and energy of γ is given by equation 11E γ = ∏ l−1 k=0 z u k ,u k+1 (11) The i th feature energy is computed by maximizing the path length to infinity. The i th feature energy is expressed in the following equation (12).…”

Section: Infinite Feature Selectionmentioning

confidence: 99%

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An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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Background/Objectives: The main objective of this work is to obtain an efficient brain tumor image retrieval and classification using Deep Neural Network (DNN). Methods/Statistical analysis: The features from the medical images are extracted by using tamura feature extraction, Local Ternary Pattern (LTP) and Histogram of Oriented Gradients (HOG). Subsequently, an Infinite Feature Selection (Inf-FS) technique is incorporated to select optimum features from feature vector, which leads to improve the classification process using sparse auto encoder based DNN. Furthermore, the retrieval performance of the proposed method is improved by Euclidean Distance technique. Findings: An Open Access Series of Imaging Studies (OASIS) and Contrast Enhanced- Magnetic Resonance Image (CE-MRI) datasets are utilized to analyze the proposed method. The sparse auto encoder based DNN classification scheme yields an overall accuracy of 95.34% in OASIS dataset and 99.87% in CEMRI dataset with improved sensitivity, specificity, error rate. The retrieval performance of proposed technique is assessed in terms of Average Retrieval Precision (ARP) and compared with two existing methods such as Local Mesh Vector Co-occurrence Pattern (LMVCoP) and Content Based Image Retrieval- Convolutional Neural Network (CBIR-CNN). The ARP of the proposed method for CE-MRI and OASIS dataset is 98.33% and 88.25% that is high when compared to the CBIR-CNN, LMVCoP method. Novelty/Applications: An appropriate feature selection using Inf-FS and DNN based nonlinear feature data classification are used in the applications of medical image retrieval.

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Section: Infinite Feature Selectionmentioning

confidence: 99%

“…The image retrieval system is generally classified as 2 types such as text-based retrieval and content-based retrieval systems (12) . Conventional method of retrieval system works by annotating text to images.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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“…Lobulation, (3).Calification, (4).Cavity &vacuolous, (5).Spiculation, and (6). Pleural dragging, (7). Bronchial Mucus Plugs (BMP), (8).…”

Section: Proposed Triplet Cbmir Systemmentioning

confidence: 99%

“… Manhattan Distance (4) It is also known as City Block Distance.  Euclidean Distance (5) It is also known as City Block Distance  Canberra Distance (6)  Distance (7) Where Q is query image, is the length of feature vectors, is the database, is the feature i in the database, is the feature i in the database of query image .…”

Section: F Similarity Computationmentioning

confidence: 99%

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Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application

Biswas*,

Roy,

Biswas

2019

IJEAT

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 Abstract: Content Based Medical Image Retrieval (CBMIR) has found its relevance in medical diagnosis by processing massive medical databases based on visual and semantic features and user preferences. In this paper we address two issues such as retrieval and recognition. We present a novel method called Triplet-CBMIR for lung nodules CT images retrieval and recognition application. A Triplet CBMIR is a combination of three properties: Visual Features (Shape and Texture), Semantic Features and Relevance Feedback. Dataset training is done using: Preprocessing, Feature Extraction, Selection, Nodules Sign Detection and Clustering. In preprocessing we perform image scaling, denoising and normalization. In feature extraction, two methods are presented such as Hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), Bounding-Box based Convolutional Network (CNN) for visual and semantic features extraction. Then optimum set of feature vectors are selected using Mutual Information based Neighborhood Entropy (MINε). Based on selected features, lung nodule sign is detected using K-nearest Neighbor (KNN) algorithm in which Hassanat Distance used and similar images are grouped using Multi-Self organizing Map (SOM). For similarity measurement, d_1 distance metric is used. Benchmark dataset such as LISS and LIDC are used for the study. Performance matrices such as Average Precision Rate (APR), Average Retrieval Rate (ARR), Average Recognition Rate (ArR), Running Time found in the simulation results are compared with some other already present state-of-the-art works. The proposed method shows a significant improvement as compared to other existing methods.

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Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation

Ling¹,

Denœux²,

Tonnelet³

et al. 2021

Machine Learning in Medical Imaging

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Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images

Cited by 45 publications

References 21 publications

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application

Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation

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