Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression

Kadam, Vinod J.; Jadhav, Shivajirao M.; Vijayakumar, K.

doi:10.1007/s10916-019-1397-z

Cited by 113 publications

(38 citation statements)

References 44 publications

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“…In 2017, an interface system developed [52] part of their study, which effectively and accurately collect and transfer the body sensor data to IoT networks and mHealth. Also, during the quarantine period, it is effectively monitoring the symptoms [53][54]. The sensor-embedded interference system transfers the optimized data to avoid data congestion in the network when it is in the case of a geographical area or a massive number of people.…”

Section: Inference Systems In Ubiquitous Sensingmentioning

confidence: 99%

IoT based Big data Analytics in Healthcare: A Survey

Aiswarya¹,

Ramesh²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Heterogeneous data surround the world, and it is generated enormously in every moment of a day from multiple sources. These enormous amounts of unstructured, structured, and semi-structured data are called big data, and this cannot store in conventional database systems. Big data, IoT plays a significant role, here big data implies the clinical data which comes in the form of medical prescriptions, laboratory data, genome database, electronic health records, medical images, medical IoT, etc. It supports clinical decisions, along with advanced and personalized health care. In this paper, a brief survey is from the related projects which deal with the challenges, techniques, as well as the different directions in big data health care.

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Section: Inference Systems In Ubiquitous Sensingmentioning

confidence: 99%

IoT based Big data Analytics in Healthcare: A Survey

Aiswarya¹,

Ramesh²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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“…Lung rectification based on convex hull technique will also fail when the nodules present in the cardiac and mediastinum region. Machine learning based segmentation algorithm is suitable for big data and sensitive based on nodule types [15] [16]. In view of confines of the segmentation algorithms in the literatures we proposed segmentation based on transition region analysis and crack code method.…”

Section: Nodule Segmentation and Classificationmentioning

confidence: 99%

A Versatile Segmentation Approach For Diagnosis of Lung Cancer

Malligeswari¹,

Kavya²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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The necessary and critical step is to evaluate the development of lung cancer and nodule segmentation. Immobile challenge in the field of segmentation and classification of pulmonary lung nodule is particularly used to identify the small size nodule. To improve and sustain the diagnosis analysis, this paper puts forward and widens a new approach to segmentation and classification method for lung nodule size less than 3mm. In this paper, we examined and proposed a new method based on transition region based P-Tile thresholding and followed by Watershed processing for segmentation. First we reap the ROI from the input CT image and enhance the region of nodule by median filtering algorithm. Second Object contours are obtained by transition region based analysis. Third to extract multiple objects ROI from object contours employ M-Type morphological operation. Fourth prepare the images for segmentation by reducing noise and smoothing operations like weighted average filter. Kuwahara filter is used to smoothen the images and to preserve the edge position. Then we make use of crack code analysis to renovate lung boundaries. Finally the result is obtained by overlap the extracted image with the restored lung mask. To evaluate the novel segmentation method examines 90 lung nodules with 3mm to 9mm diameter from LIDC database. The presented novel approach attain ground truth rate of 86.93% ±0.09 with false positive rate of 15.09% ±0.06.After evaluation and investigation the results of segmentation our proposed method outperformed compared to other literature. .

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“…So far, many Machine learning and soft computing approaches have been applied to breast cancer diagnosis problems due to their cost-effectiveness and high accuracy. The most important approaches in this filed are as follows; support vector machines (SVMs) [4][5][6], Decision trees [7][8][9], Artificial neural network (ANN) [10][11][12][13][14], Naive Bayes classifier [15], K-nearest neighbour [16], and ensemble methods [17][18][19][20]. It is undeniable that majority of the mentioned learning approaches have to deal with difficult challenges such as feature subset selection, along with the parameter tuning in their training procedure.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the noticeable success of metaheuristic algorithms in solving a lot of optimization problems in a wide range of applications, there are various types of metaheuristic algorithms include Genetic algorithm [21,22], Firefly Algorithm [23], Particle swarm optimization [24,25], Ant Colony Optimization [26], Bat algorithm [27], Whale Optimization Algorithm [28], Artificial fish swarm [29], and Grey wolf optimizer [30] has been extensively reported in recent literature. To classify breast tumors into cancerous and non-cancerous ones, an ensemble learning method was proposed by Vinod Jagannath Kadam et al [17] based on SoftMax Regression and Sparse Autoencoders. The results of their study demonstrated its efficiency for breast tumor classification.…”

Section: Related Workmentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi-Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT). Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT's parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT's main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc.) was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT-MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

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Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression

Cited by 113 publications

References 44 publications

IoT based Big data Analytics in Healthcare: A Survey

IoT based Big data Analytics in Healthcare: A Survey

A Versatile Segmentation Approach For Diagnosis of Lung Cancer

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

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