Evaluation of Machine Learning Models for Breast Cancer Diagnosis Via Histogram of Oriented Gradients Method and Histopathology Images

Kadhim, Rania R.; Kamil, Mohammed Y.

doi:10.17762/ijritcc.v10i4.5532

Cited by 6 publications

(3 citation statements)

References 1 publication

(1 reference statement)

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“…Second, the problem of the multiclass sort is complex and requires the input of several experts in their respective fields. Third, a data augmentation approach needs to be implemented to lessen the imbalance in the dataset [23]. Due to the success of CNNs in many areas, there is now a large variety of CNN designs with distinct deep characteristics and learning requirements.…”

Section: Methodsmentioning

confidence: 99%

People identification via tongue print using fine-tuning deep learning

Obaid,

Y. Kamil,

Hamza

2023

IJRES

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<span>Many person-verification systems are critical in security systems for verifying passage through doors opened to specific people using various techniques. People can use electronic payment methods and security apps to generate codes for quick, remote financial transactions. Older systems required precision and speed. Many alternative methods were developed by technology and artificial intelligence to make such operations simple and quick. The identification of tongue prints is discussed in this paper. Tongue prints, like fingerprints, are unique to each individual. The tongue was used in this study because it is unique among such organs. The tongue is protected by the lips. This guards against taking a tongue print by force. Some people distort their fingerprints, making fingerprint recognition systems unable to recognize them. Car accidents cause facial distortion, which distorts the system and prevents it from distinguishing facial prints, so the tongue was used as a fingerprint in this study. A database of 1,104 images for 138 Mustansiriyah University College of Science students yielded an average of eight images per individual. VGG16 was implemented for transfer learning and fine-tuning. In comparison to previous studies, the accuracy achieved was more than 91%.</span>

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Section: Methodsmentioning

confidence: 99%

People identification via tongue print using fine-tuning deep learning

Obaid,

Y. Kamil,

Hamza

2023

IJRES

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“…However, the model evidence p θ(X) is not computationally tractable. On the other hand, Evidence Lower BOund (ELBO) L(θ;α;X) can be maximized to estimate the way in which dataset is described by the model when Jensen's inequality is used [19] which is given as indicated in equations ( 2) and (3), log θ(x)≥ L(θ;α;X)=∑ L(θ; α; X)i 𝑁 𝑖=1…”

Section: Construction Of An Auto Encoder Hidden Markov Model (Ae-hmm)mentioning

confidence: 99%

Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model

Mahesh

Goswami

Sriramulu

et al. 2022

Int. j. commun. netw. inf. secur.

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Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score.

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“…It also has taste buds and papillae. Observing the tongue's properties, such as its color and form, is crucial in traditional Chinese medicine for illness diagnosis [7]. Recently, it has seen a rise in use as a biometrics tool.…”

Section: Introductionmentioning

confidence: 99%

People Recognition via Tongue Print Using Deep and Machine Learning

Kamil

Hamza

2023

JAIT

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The tongue is a unique organ that is well protected inside the mouth and not affected by external factors; it is also difficult to forge. Several biometric systems are widely used for authentication and recognition, such as fingerprints, faces, iris, sound, retina, etc. Traditional biometrics represent a challenge and an obstacle as they can be falsified, duplicates can be made (e.g., iris, face, fingers, signature), or they are expensive and rarely used (e.g., DNA). The increased security measures called for modern biometrics that is more secure, less expensive, and cannot be falsified. As a result, the goal of this paper is to create a system for distinguishing people based on their tongue prints. It will contribute to solving many forensic issues and increasing electronic security because it has features suitable for identification and biometrically distinguishing between people. In this paper, the tongue is located based on the fixed window size method. After tongue localization (ROI), feature extraction using the VGG-16 model, and a classification system that uses both transfer learning and machine learning as VGG-16, XGBoost, KNN, and RF classifiers, extracted features are then trained for personal identification. The dataset consisted of 1085 tongue images of 138 people with a test ratio of 20%, and the results achieved an accuracy of 92%. The process of distinguishing people through tongue prints has proven to be effective and accurate.

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Evaluation of Machine Learning Models for Breast Cancer Diagnosis Via Histogram of Oriented Gradients Method and Histopathology Images

Cited by 6 publications

References 1 publication

People identification via tongue print using fine-tuning deep learning

People identification via tongue print using fine-tuning deep learning

Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model

People Recognition via Tongue Print Using Deep and Machine Learning

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