An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney, Gökhan; Yigin, Busra Ozgode; Guven, N.; Alıcı, Yasemin Hoşgören; Çolak, Burçin; Erzın, Gamze; Saygılı, Görkem

doi:10.9758/cpn.2021.19.2.206

Cited by 6 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 Meanwhile, the A4 model, which does not apply dropout regularization, can produce the best training graphics among the other models, even though it uses eight neurons in the dense layer. This was in accordance with the opinion of Guney et al [11], that underfitting happened due to the model needing to be more complex or the number of neurons being small, accompanied by the use of regularization in the model. Thus, the model did not sufficiently learn patterns in the data as a whole.…”

Section: Training Resultssupporting

confidence: 91%

The Identification of Early Blight Disease on Tomato Leaves Utilizing DenseNet Based on Transfer Learning

Dermawan,

Awalia,

Suharso

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

Early blight disease initiated by the fungus Alternaria solani causes reduced tomato harvests of up to 86%. Identification of these diseases manually was prone to identification errors. Thus, it was required to involve deep learning to reduce oversight. This study aimed to determine the performance of the CNN pre-trained model, namely DenseNet based on transfer learning, for identifying early blight disease in tomatoes. The transfer learning technique was carried out by changing the last layer in the model used. The total image data of as many as 3,000 datasets consisting of early blight, healthy, and other disease types were divided into training, validation, and testing data. The data was trained to employ eight different modeling scenarios based on the percentage of data sharing and a combination of hyperparameter tuning. The evaluation results obtained the A4 model as the best model, which uses 2,400 training data, 300 validation data, and 300 testing data. Using a dense layer of 8 neurons, as well as the Adam optimizer with a learning rate of 0.00001. The model succeeded in obtaining validation accuracy values of 91.33%, testing accuracy of 90%, average precision of 90%, average recall of 90%, and testing time of 1.75 seconds. In addition, during the model testing process, it was found that the model was less optimal if it identified new data with conditions and background images that blended with the identified leaf objects.

show abstract

Section: Training Resultssupporting

confidence: 91%

The Identification of Early Blight Disease on Tomato Leaves Utilizing DenseNet Based on Transfer Learning

Dermawan,

Awalia,

Suharso

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Therefore, we would anticipate that the current advances in disease diagnosis technologies and data-intensive healthcare science may undoubtedly take advantage of innovative deep learning models with neuroimaging and genomics for the fields of global health, public health, and population health during the next few years [ 99 , 100 ]. As a consequence, the general public and governments would have to address open challenges and emerging issues with significant preferences in the upcoming years [ 101 , 102 , 103 ]. In the next decade to come, the diagnosis and prediction of AD concerning deep learning models with neuroimaging and genomics would be materialized for target-specific therapeutics in personalized medicine when prospective large-scale investigations are able to extensively appraise the relevant novel neuroimaging and genomics features [ 104 , 105 , 106 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

Lin

Lane

2021

IJMS

View full text Add to dashboard Cite

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

show abstract

Artificial Intelligence Based Quality Control in 3D Bio-printing

Kathirvel,

Gobinath

2024

Springer Tracts in Electrical and Electronics Engineering

View full text Add to dashboard Cite

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Cited by 6 publications

References 50 publications

The Identification of Early Blight Disease on Tomato Leaves Utilizing DenseNet Based on Transfer Learning

The Identification of Early Blight Disease on Tomato Leaves Utilizing DenseNet Based on Transfer Learning

Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

Artificial Intelligence Based Quality Control in 3D Bio-printing

Contact Info

Product

Resources

About