Classification of Histopathological Images Through Bag-of-Visual-Words and Gravitational Search Algorithm

Efficient feature selection for histopathological image classification with improved multi-objective WOA

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

A new firefly algorithm-based superpixel clustering method for vehicle segmentation

The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithm-based superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision. The modified firefly algorithm is further used to obtain the optimal superpixel clusters. The modified firefly algorithm is compared against state-of-the-art meta-heuristic algorithms on IEEE CEC 2015 benchmark problems in terms of mean fitness value, Wilcoxon rank-sum test, convergence behaviour, and box plot. The proposed meta-heuristic algorithm performed superior on more than 80% of the considered benchmark problems. Moreover, the modified firefly algorithm is statistically better on more than 92% of the total problems during Wilcoxon test. Further, the proposed segmentation method is analysed on a traffic dataset to segment the auto-rickshaw. The performance of the proposed method has been compared with kmeans-based superpixel clustering method. The proposed method shows the highest mean value of 0.6242 for Dice coefficient. Both qualitative and quantitative results affirm the efficacy of the proposed method.

New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

The progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.

An optimal nuclei segmentation method based on enhanced multi-objective GWO

In breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.

Improved bag-of-features using grey relational analysis for classification of histology images

An efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods.

A new clustering method for the diagnosis of CoVID19 using medical images

With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score.

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.