Emphysema Medical Image Classification Using Fuzzy Decision Tree with Fuzzy Particle Swarm Optimization Clustering

Narayanan, Swathi Jamjala; Soundrapandiyan, Rajkumar; Perumal, Boominathan; Baby, Cyril Joe

doi:10.1007/978-981-13-1921-1_31

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…These algorithms include decision tree, AdaBoost, MLP, K-neighbor, SVC, random forest, and Gaussian process. [51][52][53][54]63 They used about 80% of the data for training the model and only 20% for validation and testing. For evaluation of the model, they used precision, recall, and accuracy metrics.…”

Section: Discussionmentioning

confidence: 99%

An intelligent medical image classification system using few‐shot learning

Abbas

2022

Concurrency and Computation

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Summary Due to the wide range of diseases and imaging modalities, a retrieving system is a challenging task to access the corresponding clinical cases from a large medical repository on time. Several intelligent systems are developed to recognize medical images based on various machine learning algorithms such as deep learning and transfer learning methods that have outperformed compared to traditional methods. However, they are time‐consuming and required more labeled data for training the network models. To overcome these issues, an intelligence medical imaging recognition (IMR‐FSL) system is developed based on three phases of the few‐short learning (FSL) concept, which required few labeled examples to train the model and they are capable to generalize on huge and unseen medical datasets. To test and train the performance of the proposed IMR‐FSL system, many imaging modalities and multiclass datasets such as TCIA and Kvasir are utilized. Statistical analysis is also performed to measure the performance of the IMR‐FSL system. On average, the IMR‐FSL system achieved SE of 95%, SP of 96.5%, ACC of 97.5%, and AUC of 0.96 on both datasets. The performance of the IMR‐FSL system demonstrates the effectiveness of other state‐of‐the‐art systems.

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Section: Discussionmentioning

confidence: 99%

An intelligent medical image classification system using few‐shot learning

Abbas

2022

Concurrency and Computation

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“…With specific reference to images, feature selection has been carried out by different algorithms. Some examples include the utilization of a Genetic Algorithm (GA) over a data set containing nodules in lungs and breast [ 38 ] (2011), a Flower Pollination Algorithm to detect cancer in lungs [ 39 ] (2020), a Simulated Annealing scheme coupled to GA to classify brain tumors in MR imagery [ 40 ] (2019), a fuzzy Particle Swarm Optimization (PSO) scheme to deal with CT images showing examples of emphysema [ 41 ] (2019), a Bat Algorithm to tackle X-ray imagery of lungs [ 42 ] (2019), a hybrid algorithm composed of PSO coupled with fuzzy C-means to segment MR imagery [ 43 ] (2020), and an Artificial Bee Colony used for Parkinson’s disease [ 44 ] (2020). By looking at the dates of the publications just cited, it is evident that automatic feature selection in image classification is an important and still open problem.…”

Section: Related Workmentioning

confidence: 99%

A Two-Step Approach for Classification in Alzheimer’s Disease

Falco

Pietro

Sannino

2022

Sensors

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The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF–THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer’s disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.

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“…The bullae index method [42] was also gone through for emphysema classification. The simulation results are also compared with other existing methods like LBP [45],LRM [46], Feature ensemble [47], SFS [48], CNN [49], LBP [50], Fuzzy Decision Tree [51], CNN [52],JWRIULTP [53], and FFO+ELM [22] in terms of several positive or Type I measures like, "accuracy, sensitivity, specificity, precision, NPV, F1 Score, and MCC", and negative or Type II measures like, "FPR, FNR, and FDR".…”

Section: A Simulation Set-upmentioning

confidence: 99%

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

2021

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Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intraobserver variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.INDEX TERMS Adaptive local ternary pattern, fuzzy C means clustering, modified deep learning, parameter optimized-faster region convolutional neural network, pulmonary emphysema.

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Emphysema Medical Image Classification Using Fuzzy Decision Tree with Fuzzy Particle Swarm Optimization Clustering

Cited by 12 publications

References 15 publications

An intelligent medical image classification system using few‐shot learning

An intelligent medical image classification system using few‐shot learning

A Two-Step Approach for Classification in Alzheimer’s Disease

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

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