Deep Learning Classification Based Fast Segmentation for Detection of Lung Cancer

Comparative Performance Analysis of AdaBoost-Assisted Deep Learning Models for Lung Cancer Nodule Detection

Lung cancer (LC) is among the leading causes of cancer-related deaths, and early identification of pulmonary nodules plays a crucial role in reducing mortality. This work proposes a novel hybrid framework that combines preprocessing, segmentation, feature extraction, and classification for accurate nodule detection from computed tomography scans. Digital imaging and communications in medicine images are first preprocessed using median filtering to remove noise and contrast limited adaptive histogram equalization to enhance contrast. Segmentation of lung regions is performed through histogram-based thresholding and connected component analysis. To improve feature quality, a minimum repetition and a wolf search algorithm are applied for heuristic feature selection, followed by feature learning using AdaBoost. The selected features are then classified using deep learning architectures, including LeNet, AlexNet, and VGG16, with softmax for final prediction. Experimental results show that AlexNet with SGD achieved the best performance with 97.42 % accuracy and an F1-score of 97.58 %, outperforming Adam and LeNet. Although LeNet (SGD) reached 95.9 % accuracy, its sensitivity (94.76 %) was slightly lower than that of AlexNet. The Adam optimizer provided competitive results but generally underperformed compared to SGD in both architectures. Overall, SGD-optimized AlexNet offers the most reliable balance of sensitivity, specificity, and predictive values for LC nodule detection. This integration of ensemble learning and deep neural networks enhances detection accuracy, reduces reliance on handcrafted features, and offers a robust solution to support radiologists in early LC diagnosis.

Multi-class lung cancer detection and classification from CT scans using CNN transformer

Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model

This study concludes that integrating VGG16 and U-Net+ into this CNN architecture can significantly enhance lung cancer detection performance by achieving a remarkable accuracy of 96% and provide a reliable tool for clinicians in early stage diagnosis and treatment monitoring.

Efficientnet-B0-Based Deep Learning Framework for Automated Lung Cancer Detection Using Chest Computed Tomography Images

However, early-stage lung cancer is one of the major causes of death from cancer due to its symptoms being absent or hard to detect by conventional clinical analysis. The late diagnosis of this disease causes the effectiveness of the treatment to become poor and low survival rates. Traditional interpretation of medical imaging involves manual analysis which makes it burdensome, prone to variation between observers, and inefficient for screening in cases where many chest CT images need to be analyzed. This calls for efficient computer-based intelligent diagnostics systems that can provide effective, reliable, and fast classification results. In this paper, an EfficientNet-B0-based deep learning model for automated lung cancer classification using chest CT images is proposed. Preprocessing steps such as normalization, lung segmentation, resizing, and data augmentation are used. The custom dataset, specifically PulmoVision Lung Cancer CT Dataset (PLCD-2026), contains balanced image samples that correspond to healthy lungs, benign nodules, and malignancies in order to provide sufficient conditions for effective model training. EfficientNet-B0 learns discriminative anatomical and pathological features and remains computationally efficient by means of optimized network scaling. High classification capabilities of the developed approach can be proved by means of the evaluation procedure conducted in terms of accuracy, precision, recall, and F1-score, which proves reliable identification of different pulmonary diseases. Grad-based visualization is additional confirmation of proper localization of suspicious areas, contributing to the interpretability of the classification process. The developed framework provides an efficient tool for lung cancer detection, helps doctors to make decisions, saves their time, and ensures consistent clinical assessment.

Deep Learning-Based Lung Cancer Diagnosis and Classification Using CT Images

One of the main causes of cancer-related fatalities globally is lung cancer, and increasing patient survival requires early diagnosis. Lung cancer screening frequently uses computed tomography (CT) imaging, although manual interpretation can be laborious and reliant on radiologist skill. A deep learning-based framework for CT image-based lung cancer diagnosis and classification is presented in this work. Lung CT scans are divided into three groups by the suggested system: benign, malignant, and normal. Preprocessing methods including Intensity-based feature enhancement, Contrast Limited Adaptive Histogram Equalization (CLAHE), and normalizing are used to improve image quality and highlight significant features. Both local texture features and global contextual information are extracted from CT images using a hybrid deep learning model that combines ResNet18 and Vision Transformer (ViT). Additionally, data augmentation is used to enhance model generalization and lessen class imbalance. The Hybrid-RViT model, which combined ResNet18 and ViT, outperformed the competition with 97.13% accuracy. These findings show that the suggested methodology can efficiently facilitate automated lung cancer diagnosis and help physicians make decisions more quickly and accurately.

A Comprehensive Review of Hybrid Morphological and Deep Learning Techniques for Pancreatic Cancer Detection and Stage Prediction

Pancreatic cancer is one of the most aggressive and life-threatening malignancies, marked by late diagnosis, rapid progression, and poor survival rates. Accurate detection and stage prediction remain difficult due to the pancreas's complex anatomy, indistinct tumor boundaries, and variability in medical imaging data. Recent advancements in medical image analysis increasingly rely on integrating image processing techniques with deep learning models to improve diagnostic performance. This review evaluates existing morphological operations and deep learning architectures to detect, segment, and predict pancreatic cancer stages. Preprocessing techniques, such as erosion, dilation, opening, and closing, are widely used for noise removal, contrast enhancement, and boundary refinement. In addition to enhancing image quality, these methods facilitate the extraction of features more effectively. In medical images, deep learning models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid CNN-Transformer frameworks have demonstrated strong capabilities to capture both local spatial features and global contextual relationships. A critical evaluation of state-of-the-art approaches is presented, along with their strengths and limitations. There are several key challenges identified, including the use of large annotated datasets, limited generalization across imaging modalities, and an insufficient integration of multi-modal data. Furthermore, most studies focus primarily on detection and segmentation, with relatively less attention paid to accurate stage-wise classification of pancreatic cancer. Several promising research directions are highlighted, including self-supervised learning, multimodal data fusion, explainable artificial intelligence, and 3D volumetric analysis. Overall, this review offers a structured overview of current advancements and identifies critical research gaps, which provides valuable insights for developing robust, efficient, and clinically applicable computer aided diagnostic systems for early detection and stage prediction of pancreatic cancer.