An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan, K S

doi:10.17485/ijst/v13i39.1621

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…Dataset Accuracy (%) VGG16 (1) Computed tomography (CT) 97.6 KNN's With GLCM (2) Chest CT images of COVID 98.9 Sparse Auto Encoder based DNN (8) OASIS 95.34 ICNN (9) Pap smear dataset 98.88 HoG-LTP method with CNN-Classifier (10) CE-MRI 98.8 GIPBT + DKD (11) Medical Image Set 98.4 CNN (18) liver tumors CT images 96.55 CNN-based deep learning techniques -ResNet-18 (21) GPD data set 96.21 DeepSVM (19) NCT-CRC-HE-100 K 98.75 Hybrid models (CNN, VGG16 and VGG19) (16) diabetic retinopathy (DR) 90.6 CNN-sequential model (20) Chest X-ray (1000 images) 98.437 Proposed Hybrid Model CNN-LSTM Medical Image Dataset 99. 4 The performance of ANN, CNN and Hybrid Model for the medical dataset considered in this work shown in Figure 6, the performance of proposed ensemble method provides more accuracy in image classification compared with ANN and CNN, because of more dense layers of CNN and additional layers of LSTM, also accurate feature extraction with GLCM.…”

Section: Resultsmentioning

confidence: 99%

“…More and more medical images are appearing in experimental diagnosis due to the fast expansion of the medical profession and the ongoing development of medical technology. The study of medical images is easy now to identify these images properly and accurately (8) . Due to variation in the shape and size of the images, the retrieval task becomes more monotonous in the large medical databases (9) .…”

Section: Introductionmentioning

confidence: 99%

“…A variety of sets of images and datasets in existing techniques perform medical image retrieval such as, Computed tomography (CT) (1) , Chest CT images of COVID (2) , CE-MRI and OASIS dataset (8) , CE-MRI (10) , Diagnosis of Liver Tumour (18) , CRC-5000, CE-MRI, Diabetic Retinopathy (DR) (16) , NCT-CRC-HE-100 K (19) , X-ray images and Computerized Tomography (CT) scans, including pneumonia, tuberculosis, and lung cancer (20) etc., are used in the current approaches to perform medical image retrieval. The following are the primary contributions of this study extracting additional features in terms of parameters such as distance, angle, resolution, level, and symmetric values.…”

Section: Introductionmentioning

confidence: 99%

“…Accuracy, confusion matrix, and F1 scores are compared with prior models; average precision values are increased with larger deviation. K Chethan et al (2023) (8) Local Ternary Pattern (LTP), and Histogram of Oriented Gradients (HOG) are used to extract the features from the medical images. Therefore, an Infinite by incorporating the Feature Selection (Inf-FS) technique, the classification process utilizing the sparse auto encoder based DNN is improved by choosing the best features from the feature vector.…”

Section: Introductionmentioning

confidence: 99%

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An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

Vasudeva,

Chandrashekara

2023

IJST

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Objectives:The objective of this work is to obtain an efficient medical image retrieval and classification from a larger healthcare datasets using Novel approach. Methods: In this study five different classes of Medical images are taken for input, features are extracted using GLCM (Grey Level Co-occurrence Matrix) by image attributes such as dissimilarity, correlation, homogeneity, contrast, ASM, and energy. The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers. The received feature vectors are input into the most often used deep learning models Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) for image classification. Then CNN model is integrated with a deep learning model based on Long-Short Term Memory (LSTM), which incorporates additional layers into its structure and works on large datasets. Further the retrieval performance is improved by Euclidean Distance Technique. Findings: Performance evaluation is performed by comparing and analyzing the experimental findings of proposed methods, ANN, CNN and CNN-LSTM yields the retrieval accuracy of 97.79%, 98.78% and 99.4%. The Precision, Recall and F1-Score are also compared, and they are more accurate when picture classification is performed on larger healthcare datasets. Novelty: The additional feature extraction using GLCM and the proposed hybrid model can extract better medical image features, and achieve higher classification accuracy compared with earlier image classification models.

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