An Efficient Mammogram Image Retrieval System Using an Optimized Classifier

Rayen, Sonia Jenifer; Subhashini, R.

doi:10.1007/s11063-020-10254-3

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Artificial neural network (ANN), support vector machine (SVM), and adaptive neurofuzzy inference system (ANFIS) are computational techniques that find broad applications in different fields of science, engineering, and economics [23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi

Ziyaee¹,

Ahmadi

Bazyar

et al. 2021

Agronomy

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Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major concern to protect health, secure food and feed, and preserve their value. The effectiveness of image processing, combined with computational techniques, has been investigated to detect and segregate peanut (Arachis hypogaea L.) seeds infected with an aflatoxin producing fungus. After inoculation with Aspergillus flavus, images of peanuts seeds were taken using various lighting sources (LED, UV, and fluorescent lights) on two backgrounds (black and white) at 0, 48, and 72 h after inoculation. Images were post-processed with three different machine learning tools: the artificial neural network (ANN), the support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) to detect the Aspergillus flavus growth on peanuts. The results of the study show that the combination of LED light and a white background with ANN had 99.7% accuracy in detecting fungal growth on peanuts 72 h from infection with Aspergillus. Additionally, UV lights and a black background with ANFIS achieve 99.9% accuracy in detecting fungal growth on peanuts 48 h after their infection with Aspergillus.

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Section: Introductionmentioning

confidence: 99%

Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi

Ziyaee¹,

Ahmadi

Bazyar

et al. 2021

Agronomy

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show abstract

“…Several medical CBIR systems have been proposed in the previous few years. Most developed imaging systems use a specific imaging modality and retrieval methods [1][2][3][4][5]. One way to help retrieval systems find relevant medical images in huge image collections is to provide them the ability to determine the image class before similarity matching.…”

Section: Introductionmentioning

confidence: 99%

Mammogram Image Retrieval System Using Texture and Semantic Features

Sharma

2022

J. Phys.: Conf. Ser.

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Image retrieval systems might help radiologists in aiding their diagnostic decision-making by giving a way to discover and identify similar images from databases. Keyword-based search became the dominant paradigm for searching multimedia datasets in the early years of image retrieval. However, using keywords alone has numerous drawbacks: human annotation is time-consuming and intrinsically incomplete, and the relationship between words and concepts is sometimes complicated. These significant challenges have motivated research in the field of Content-Based Image Retrieval (CBIR). However, using visual content has its own set of restrictions, owing to the Semantic Gap, which describes the disparity between low-level information that can be extracted quickly from images and high-level descriptions that are meaningful to users. As a result, a system with integrated approaches is important. In this paper a mammogram image retrieval system based on low level visual features and high-level semantic features has been proposed. Based upon the proposed framework, a prototype of mammogram image retrieval system has been developed. The proposed framework for retrieval of mammograms consists of two phases. The system first exploits the textual features and then further refines the search using visual features. Further, if the user is unsatisfied with the search results, he or she can send Relevance Feedback (RF) to the retrieval system, which has methods to learn about the user’s information needs. A system like this might be utilised for Computer Aided Diagnosis, medical education, and research. The framework established is generalizable and adaptable to a variety of anatomic and diagnostic circumstances. The proposed system’s performance is demonstrated by the experimental findings.

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Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

Selvi¹,

Thilagamani²

2023

Intelligent Automation &Amp; Soft Computing

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Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman filter and scale-invariant feature transform (SIFT) for feature extraction with Crow Search Optimizationbased the deep belief network (CSO-DBN). This proposed technique decreases the complexity, cost, energy, and time consumption. Training the proposed model using a deep belief network and validation is performed. Finally, the testing process gives better performance compared to existing techniques. The accuracy rate of the proposed work CSO-DBN is 0.9344, whereas the support vector machine (SVM) (0.5434), naïve Bayes (NB) (0.7014), Butterfly Optimization Algorithm (BOA) (0.8156), and Cat Swarm Optimization (CSO) (0.8852).

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An Efficient Mammogram Image Retrieval System Using an Optimized Classifier

Cited by 3 publications

References 20 publications

Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi

Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi

Mammogram Image Retrieval System Using Texture and Semantic Features

Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

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