Intelligent Cancer Detection System

Das, Utpol Kanti; Sikder, Juel; Salma, Umme; Anwar, Afsah

doi:10.1109/conit51480.2021.9498410

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Relu is an activation function that determines which information should fire and which shouldn't fire in the forward direction. The dropout layer is responsible for reducing model over-fitting and under-fitting [16]. After Convolution layers flattened layer is applied.…”

Section: ) Load Datasets and Image Categoriesmentioning

confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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At the same time, recognizing the emotions, gender and age of a person is a vital issue in real life. This research focuses on identifying the emotions along with age and gender of different ages male, female, common gender and children. In this study, a modified convolutional neural network (M-CNN) has been proposed for feature extraction where classification is followed by a Multi Support Vector Machine (M-SVM). Instead of detecting the face, a head-detecting technique is also used in the prediction part. To detect the head we proposed a system using the You Only Look Once Version 3 (YOLOV3) network. It helps to extract the feature from the whole head instead of the only face. Three datasets named AR-2022, FER-2022 and GR-2022 are introduced in this study. We also include the common gender in our datasets. In the case of age classification, we categorized the age datasets into 11 categories depending on the similarity of their features, gender into 3 categories and emotions into 7 categories. Our proposed model is compared with other existing models and current research, where the proposed model gives better accuracy and the accuracy of classification of emotions, age and gender are 97.47%, 98.43% and 96.65% respectively.

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Section: ) Load Datasets and Image Categoriesmentioning

confidence: 99%

An Approach Based on Deep Learning for Recognizing Emotion, Gender and Age

Nippon

Juel

2022

Preprint

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“…There are many medical support systems in engineering. For example, systems for medical professionals include cancer diagnosis, brain infarction diagnosis, and community medical support [ 3 , 4 , 5 , 6 ]. Additionally, for non-medical personnel, there are health management systems using smart watches, AI-based disease identification applications, and applications for diagnosing the possibility of diabetes [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Data Analysis and System Development for Medical Professionals on Sleep Apnea Syndrome and Orthostatic Dysregulation by Processing-Healthcare Professionals and Patients

Shirasuna

2022

Sensors

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This paper presents the details of our research and the activities involved. Japan is one of the most advanced countries in medicine worldwide. However, in terms of technology, knowledge sharing, and successor development, Japanese medicine lags behind other developed countries, and these matters require addressing. The country is also facing a shortage of doctors, among other things, and this medical problem will surely become critical in the near future. In this study, we aim to help solve such problems from the medical engineering viewpoint, analyze and create systems based on the experience of doctors from the engineering viewpoint, and make it easy for patients to understand orthodox and general statistical analysis methods. We perform a visualization and quantitative medical data analysis and examine diagnostic support. We consider sleep apnea syndrome (SAS), and orthostatic dysregulation (OD) in children in this study. This research aims to detect SAS early, identify people with pre-SAS who are likely to become SAS in the near future, and identify OD. We analyze and identify these diseases through statistics and a multivariate analysis and create a dedicated analysis system for them. Our research and system development will allow specialists to make informed diagnoses, reproduce empirical rules, improve work efficiency, and improve patients’ health awareness. This research has only looks at two diseases, but these methods can be expected to be applied to other diseases.

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