Deep Learning Techniques for Lung Cancer Recognition

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(4), P. 14916 - 14922

Published: Aug. 2, 2024

Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance survival depends on early diagnosis nodules. Manual screenings human factor. The variability in size, texture, and shape nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle nodule detection. goal was improve prediction accuracy by exploring performance multiple transfer learning models instead relying solely deep models. An extensive dataset CT scans gathered train built research paper focused Convolutional Neural Networks' (CNNs') ability automatically learn adapt discernible features images which particularly beneficial classification, aiding identifying true false labels, ultimately enhancing diagnostic accuracy. provides comparative analysis CNN, VGG-16, VGG-19. Notably, model VGG-16 achieved remarkable 95%, surpassing baseline method.

Language: Английский

---

A Deep Learning Approach to Unveil Types of Mental Illness by Analyzing Social Media Posts

Mathematical and Computational Applications, Journal Year: 2025, Volume and Issue: 30(3), P. 49 - 49

Published: May 3, 2025

Mental illness has emerged as a widespread global health concern, often unnoticed and unspoken. In this era of digitization, social media provided prominent space for people to express their feelings find solutions faster. Thus, area study with sheer amount information, which refers users’ behavioral attributes combined the power machine learning (ML), can be explored make entire diagnosis process smooth. study, an efficient ML model using Long Short-Term Memory (LSTM) is developed determine kind mental user may have random text made by on media. This based natural language processing, where prerequisites involve data collection from different sites then pre-processing collected per requirements through stemming, lemmatization, stop word removal, etc. After examining linguistic patterns posts, reduced feature generated appropriate engineering, further fed input LSTM identify type illness. The performance proposed also compared three other models, includes full one. optimal resulting selected training testing all models publicly available Reddit Health Dataset. Overall, utilizing deep (DL) analysis offer promising avenue toward improved interventions, outcomes, better understanding issues at both individual population levels, aiding in decision-making processes.

Language: Английский

---

A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(5), P. 17324 - 17329

Published: Oct. 9, 2024

One of the most common life-threatening diseases, brain tumor is a condition characterized by rapid proliferation abnormal cells that leads to destruction healthy cells. Its aggressive nature can result in patient succumbing disease before an accurate diagnosis achieved. Timely detection crucial effective treatment and survival. Similarly, early plays pivotal role case tumors, where swift identification vital providing optimal care increasing chances recovery. Streamlining complex process significant undertaking aims simplify expedite procedure, ultimately contributing saving valuable time enhancing outcomes. The proposed model, modified VGG-16, facilitates faster more cells, leading tumors. A novel multihead self-attention mechanism used VGG-16 architecture improve performance. model performs better than other state-of-the-art models, such as normal ResNet-50, EfficientNet.

Language: Английский

---

Multiclass Classification of Mental Health Disorders Using XGBoost-HOA Algorithm

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Dec. 12, 2024

Language: Английский

---

Emotion Aware AI for Mental Health Monitoring

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 63 - 69

Published: Nov. 9, 2024

Mental health challenges like depression, anxiety, and stress are increasingly common in today’s fast-paced world. Early detection consistent monitoring of emotional states essential for timely support. This report outlines the development an Emotion-Aware AI system that tracks evaluates individual’s well-being real time. By integrating advanced machine learning models deep neural networks, analyzes facial expressions, voice tones, text data to provide a holistic understanding user’s state

Language: Английский

---