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

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

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

Pal

Saraswat

Mittal

2021

Self Cite

“…To tackle these challenges, nature-inspired algorithms have proven a successful solution for generating efficient clusters [27,28]. Such algorithms have solved many optimization problems of real-world [29][30][31][32]. Some of the popular nature-inspired algorithms are genetic algorithm (GA) [33], biogeographybased optimization (BBO) [34,35], salp swarm optimization [36], gravitational search algorithm (GSA) [37], whale optimization algorithm (WOA) [38], and grey wolf optimizer (GWO) [39].…”

Section: Introductionmentioning

confidence: 99%

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

Sharma

2021

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.

“…K-means, a widely used clustering approach, has been used in a number of engineering domains for the same. However, K-means generates biased clusters due to its dependence over parameter settings and initial cluster centres [6]. To remedy this concern, meta-heuristic-based solutions have been widely employed to obtain optimal cluster centroids in the last two decades [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Tripathi

Mittal

Saxena

et al. 2020

Self Cite

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.