An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

Sharmili, Kasturi Chandrahaasan; Suja, G P; Elumalai, Preetham; Walid, Md. Abul Ala; Arunachalam, Sasi; Babu, G. Charles

doi:10.1109/iciccs56967.2023.10142306

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Finally, the recognition is made using the independent test dataset. The architecture of the proposed CNN model is inspired by ResNet50 [26]. However, for our proposed CNN model, we used the architecture of improved ResNet50 introduced by Wu et al [27].…”

Section: Methodsmentioning

confidence: 99%

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Kumer Paul,

Ala Walid,

Rani Paul

et al. 2024

Bulletin EEI

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Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.

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Section: Methodsmentioning

confidence: 99%

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Kumer Paul,

Ala Walid,

Rani Paul

et al. 2024

Bulletin EEI

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“…One of the numerous histological patterns that pathologists have linked to the disease, such as the osteoblastic, chondroblastic, or fibroblastic pattern, may have an impact on the diagnosis of osteosarcoma. CNNs distinguish between non-tumor and tumor tissues based on the inherent differences in pixel intensity patterns and spatial features within medical images [59], such as histological images, used in this study. CNNs leverage their ability to automatically learn and extract distinctive features from the images during training.…”

Section: Setup Of Proposed Cnnmentioning

confidence: 99%

“…These metrics serve as significant benchmarks for assessing the results of the experiment. Accuracy is the ratio of the sum of two accurate predictions (True Positive (TPOS) and True Negative (TNG)) and the total number of data sets (TPOS, TNG, False Positive (FPOS) and False Negative (FNG)) [59]. The accuracy of the model ranges from 1, indicating optimal performance, to 0, indicating minimal effectiveness.…”

Section: Performance Measurementioning

confidence: 99%

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Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

Walid,

Mollick,

Shill

et al. 2023

Diagnostics

Self Cite

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The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.

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ALSA-3: Customized CNN model through ablation study for Alzheimer's disease classification

Assaduzzaman,

Dutta,

Saha

et al. 2024

Informatics in Medicine Unlocked

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An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

Cited by 12 publications

References 15 publications

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

ALSA-3: Customized CNN model through ablation study for Alzheimer's disease classification

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