Deep-Learning-Empowered Breast Cancer Auxiliary Diagnosis for 5GB Remote E-Health

Mushroom is one among the most popular consumed food in India. In India people are cultivating mushroom as viable income source for their livelihood. Now-a-days deep learning is being applied to process big data and vision related applications. Recent smart devices can be utilized for automated edibility diagnosis of mushroom using deep convolution neural network (CNN) it has revealed a remarkable performance capability in all its sphere of research activities. DCNN works on static dataset. The models on which it applies will pose as well determine its requirement for training. This paper presents a classification tool for edibility detection of mushroom through deep CNN. Better performance is obtained by tuning the hyper-parameters and through adjustments in pooling combinations in order to obtain real time inference suitably. DCNN has been trained with a data set of segmentation as train and test sets. Performance is analyzed on sNet, Lenet, AlxNet, cNET network architectures. DCNN results are comparatively better in its performance.

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Identification of Edible and Non-Edible Mushroom Through Convolution Neural Network

Devika¹,

Karegowda²

2021

“…The proposed FIUIAS used centroid method for de-fuzzification and is the final step of inferencing. Here a mapping of fuzzy set to crisp set takes place [25][26][27][28][29][30][31][32].…”

Section: Defuzzificationmentioning

confidence: 99%

Design of a Fuzzy Inference Based Ultrasound Image Analysis System for Differential Diagnosis of Thyroid Nodules

Poornima¹,

Gowda²,

Pushpalatha³

2021

This paper presents a Fuzzy Inference based Ultrasound Image Analysis System for differential diagnosis of Thyroid Nodules (TNs). Thyroid Ultrasound (TUS) images containing TNs are preprocessed to remove speckle noise and are enhanced using histogram equalization method. Nodule boundaries are identified using the canny edge detection technique and required Region of Interest is obtained using Adaptive Regularized Kernel Fuzzy C-means (ARKFCM) segmentation algorithm. Nineteen texture features are extracted from the segmented images. Best First (BF), Genetic Search (GS) and Greedy Step Wise (GSW) search methods are applied to select best subset of features. Selected features are fuzzified. A novel, fuzzy system is built to discriminate benign from malignant TNs by employing Mamdani model to draw inferences and centroid scheme for defuzzification. Class Based Association (CBA) concept is used to generate fuzzy inference rules. The developed multiple input, single output FIUIAS resulted in an accuracy of 98%.

“…A classification system could be designed to leverage this information in order to predict the presence of mental disorders. For instance, if a person is showing psychotic and schizotypal symptoms together, then the researchers can conclude that the person has high probability of OCD as suggested in [27][28][29][30]. Such observations when combined with speech processing systems will be helpful in designing more complex classification systems.…”

Section: Table 3 Body Parameter Variation On the Mental State Of A Personmentioning

confidence: 99%

Analysing Mental Health Evaluation Techniques from a Statistical Perspective

Koushik¹,

Deepu²

2021

Mental wellbeing is an epitome of success for human beings. Recently, a lot of mental diseases that include anxiety, suicidal tendency, depression, bipolar disorder, obsessive compulsive disorder (OCD), etc. have deep-seated among all age groups. This is due to the fact that the social complexity has increased at work, in relationships, at home and in majorly all aspects of human life. Early detection of these problems can help in treating and if possible, eradicating this illness from a person's life. Researchers have proposed different techniques that can be put into action to effectively identify these diseases. These techniques utilize different activities of normal day-to-day human behaviour, like speech analysis, social media behavioural patterns, visual-activity pattern analysis, etc. Due to a large variety of available techniques, researchers find it difficult to standardize techniques for detection and evaluation of such diseases. Thus, in this paper we have performed an in-depth study about the state-of-the art methods to identify mental health issues. Readers will be able to get a bird's eye view of these methods and will be able to identify the best practices involved in mental health analysis. Moreover, this survey also recommends some possible improvements in the existing methods, in order to further improve the system's performance.