Lung cancer remained one of the leading causes of cancer-related mortality worldwide, making early diagnosis essential for improving treatment opportunities and patient outcomes. Computed tomography (CT) imaging became an important diagnostic modality because of its ability to capture detailed anatomical information from the pulmonary region. At the same time, advances in artificial intelligence and deep learning created opportunities for automated medical image analysis and decision support. Despite this progress, challenges, including limited labelled datasets, moderate generalisation capability, computational constraints, reproducibility concerns, and limited interpretability, continued to influence the development of reliable diagnostic frameworks. This research developed a transfer learning framework based on ResNet50 for lung cancer detection using CT scan images, evaluated classification performance through accuracy, precision, recall, and F1-score, and established a reproducible experimental baseline for future explainable and optimised diagnostic systems. A publicly available CT image dataset containing four diagnostic categories was utilised. Data preparation included image resizing, normalisation, and augmentation before model training. The experimental outcomes demonstrated progressive learning behaviour throughout training, where training accuracy reached 71.32% and validation accuracy reached 62.50%, accompanied by decreasing training and validation loss values. Evaluation on the testing dataset produced an overall classification accuracy of 57%, with weighted precision, recall, and F1-score values of 0.57, 0.57, and 0.50, respectively. Class-level analysis indicated stronger recognition performance for normal CT images and comparatively balanced detection capability for adenocarcinoma, whereas lower performance was observed for large cell carcinoma and squamous cell carcinoma. The findings suggested that transfer learning remained a practical approach for CT-based lung cancer classification under limited data conditions. In addition to experimental evaluation, the study contributed a reproducible implementation framework that may support future comparison, explainability integration, and continued optimisation of diagnostic models.

Keywords : Lung Cancer Detection, CT , ResNet50, Transfer Learning, DL, Medical Image Classification.

Authors : J.V. G. Prakasa Rao Pyla , D. Mabuni

Title : Lung Cancer Detection from CT Images Using ResNet50

Volume/Issue : 2026;3(3 (July - September))

Page No : 8 - 18

With the rapid expansion of social media and other digital platforms, the dissemination of false information through these channels has become widespread, leading to difficulties in deter-mining the authenticity of online content. Moreover, fact-checking through traditional means is very slow and cannot efficiently manage the enormous volume of information. This paper offers a deep learning-based misinformation detection system as a solution to this problem. According to the paper, the system utilizes various NLP (Natural Language Processing) techniques such as text cleaning, tokenization, and feature extraction in order to convert text data into a form that can be easily used for learning by deep learning algorithms. A Long Short-Term Memory (LSTM) deep learning model is used as the core-based encoding mechanism to capture the underlying semantics and contextual clues hidden in the text that help determine whether the text is genuine or fake. The system was subjected to a rigorous evaluation process using well-known labelled datasets while relying on widely-accepted performance metrics including accuracy, precision, recall, and F1-score. Results from the experiments indicate that the system proposed in this paper not only outperforms existing systems in terms of accuracy but also embodies an efficient, scalable, and trustworthy method for automatically detecting fake news.

Keywords : Fake News Detection, DL, NLP, LSTM, ML, Social Media Analysis, Semantic Analysis.

Authors : Md. Bushra , G Venkata Ravali , Sk. Akbar

Title : Detecting Misinformation using Deep Learning Techniques

Volume/Issue : 2026;3(3 (July - September))

Page No : 1 - 7