AI Doctor: An Intelligent Approach for Medical Diagnosis

Das, Sumit R.; Biswas, Suman; Paul, Aditi; Dey, Aritra

doi:10.1007/978-981-10-3953-9_17

Cited by 33 publications

(22 citation statements)

References 6 publications

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“…Figure 3 represents the horizontal detail coefficients of Figure 3a extracted using 2D-DWT. I Wr , I Wg , and I Wb shows the information present in three separate channels calculated using Equation (8). In this study, we took only the horizontal detail coefficients into account and used them as a set of features drawn out from the original histopathological images.…”

Section: Extraction Of 2d Wavelet Featuresmentioning

confidence: 99%

“…Computer Science has probably advanced the most in the last fifty years compared to other branches of science and technology. Machine Learning (ML) has a wide range of applications in Pathology, ranging from the detection of diseases to intelligent systems, which, judging by the patients’ symptoms, can prescribe conventional medicines [ 8 ]. The latter area is still in its infancy, and a lot of research works need to be done before such applications can be relied on for clinical practices.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

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Section: Extraction Of 2d Wavelet Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

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“…This approach has been adopted as evidenced in Multi Agent System (MAS) particularly [56]. MAS has been a center of attention in modern years as it has been effectively functional by researchers with extensive applications in different sectors including health care [57][58][59], e-Commerce [56,[60][61][62], military support [63,64], knowledge management [65][66][67][68], decision support [69][70][71][72], as well as control systems [73][74][75][76]. Fig.…”

Section: B (Case 2) Hybridizing Elm-mas (Extreme Learning Machine Anmentioning

confidence: 99%

Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

et al. 2020

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Extreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM's capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory.

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“…In the medical field, AI has also been integrated into existing healthcare systems [Szolovits (2019)], such as drug development, medical image recognition, and clinical diagnosis, etc. So far, researchers have proposed a variety of AI-based complementary medical technologies that can provide doctors with some advice in disease diagnosis [Das, Biswas and Paul (2018)], thereby solving the problem of medical equipment and manpower shortage. At the same time, research related to drug development is also underway.…”

Section: Introductionmentioning

confidence: 99%

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Yu¹,

Xia²,

Fei³

et al. 2020

Computers, Materials &Amp; Continua

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Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

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AI Doctor: An Intelligent Approach for Medical Diagnosis

Cited by 33 publications

References 6 publications

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

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