Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer

Sekaran, Kaushik; Chandana, P.; Krishna, N. Murali; Kadry, Seifedine

doi:10.1007/s11042-019-7419-5

Cited by 97 publications

(48 citation statements)

References 10 publications

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“…[25] Saba, Tanzila, et al proposed an automated cascaded design for skin lesion detection, which consisted of three significant steps, namely, contrast stretching and boundary extraction using CNN, and finally extracting depth features using transferred learning. Sekaran, Kaushik, et al [26] proposed a model deployed using a convolutional neural network to isolate infected images from healthy ones. Further, the Gaussian Mixture Model with EM algorithm is used to get the statistics regarding the percentage of cancer spread so far.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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“…The text to voice generation application can also be integrated to support the kissan application. The IOT [20] operations can be incorporated to provide the information via SMS or a phone call.…”

Section: Disease Predictor Enginementioning

confidence: 99%

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

Chandana

Ghantasala²,

Jeny

et al. 2020

IJECE

Self Cite

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The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

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“…The method was introduced by Kennedy and Eberhart in 1995 as a stochastic population-based algorithm, which is known by features like trying to find global optimize point and easy implementation with taking a small amount of parameters in adjusting process. It takes benefit from a very productive searching algorithm, which makes it a best tool to work on different optimization research area and problems [59].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The balance between global and local search can be adjusted by adopting different inertia weight. One of critical success factors in PSO is a trade-off between global and local search in iteration [59]. Artificial neural network, pattern classification, and fuzzy control are some area for deploying PSO [5].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…Predictive analytics offered by big data analytics works on developing predictive models to analyze large volume data both structured and unstructured with the goal of identifying hidden patterns and relations between variables in near future [76]. Big data analytics can help cognitive computing, and behavior pattern recognition deploys deep learning technique to predict future action as it is used to predict cancer in health care system [59]. It also leads organizations to understand their problems [13].…”

Section: Future Research Directionsmentioning

confidence: 99%

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Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer

Cited by 97 publications

References 10 publications

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

Literature Review on Big Data Analytics Methods

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