Image Enhancement Techniques Research Articles

Image Contrast Enhancement Using Artificial Hummingbird Algorithm

Abstract: Image enhancement plays an important role in medical imaging, satellite image processing, and computer vision. These fields require high-quality images to improve the visibility of details, which is essential for accurate analysis and interpretation. Contrast adjustment, histogram equalization, and gamma correction are the traditional image enhancement techniques that are widely used. However, these methods often fall short, particularly when applied uniformly across diverse images, as they may not always yield the best results. We provide a unique picture enhancing method based on the Artificial Hummingbird Algorithm (AHA) to overcome these drawbacks. The AHA is inspired by the foraging behaviour of hummingbirds, which are known for their efficient and adaptive search strategies. This bio-inspired algorithm leverages strategies to optimize the gamma correction parameter, aiming to enhance image contrast and overall visual quality. The aim is to optimize the gamma correction parameter to maximize fitness values derived from several image quality metrics, including Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), and Peak Signal-to-Noise Ratio (PSNR). The AHA operates through a population-based approach, where each individual in the population represents a potential solution with a specific gamma value. Initially, a population of random gamma values is generated, and each value is used to enhance the image, followed by evaluating its quality using the combined fitness function. The algorithm uses three distinct foraging strategies to efficiently explore the solution space: guided, territorial and migration foraging. Guided foraging helps steer the search towards promising areas, while territorial foraging refines the local search. Migration foraging periodically resets some individuals to avoid premature convergence and ensure a comprehensive exploration of the solution space. Through iterative adaptation and evolution, the AHA continuously updates and refines the best gamma value found, leading to significantly enhanced images. Experimental results demonstrate that the AHA-based technique offers substantial improvements in image quality, with better contrast, sharper details, and higher scores in the quality metrics compared to traditional methods, making it a robust and efficient solution for high-quality image processing tasks.

Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization

In medical imaging, accurate diagnosis heavily relies on effective image enhancement techniques, particularly for X-ray images. Existing methods often suffer from various challenges such as sacrificing global image characteristics over local image characteristics or vice versa. In this paper, we present a novel approach, called G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization), which perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness to preserve local and global characteristics. Experimental results show that it can significantly improve current state-of-the-art algorithms to effectively address their limitations and enhance the contrast and quality of X-ray images for diagnostic accuracy.

AI-POWERED IMAGE ENHANCEMENT IN FORENSIC APPLICATIONS: CHALLENGES AND OPPORTUNITIES

Recent progress in machine learning (ML) and computer vision has markedly enhanced the performance of automated facial recognition systems. Nonetheless, forensic applications frequently face issues with poor-quality and low-resolution images that pose challenges even to the most advanced facial recognition technologies. This research explores the potential of neural-based image enhancement and restoration techniques to recover degraded images while maintaining original correspondences for legal use. We evaluate super-resolution and deconvolution methods across two comprehensive and varied facial datasets. Our study employs twelve distinct GAN- and diffusion-based enhancement techniques. Our results offer insights and recommendations for the effective application of image enhancement in forensic facial recognition, highlighting the advantages and potential limitations of these advanced technologies.

PEFusion: An infrared and visible light fusion method for enhanced representation of prominent targets and exposure errors

Infrared and visible light fusion is a pivotal technique for image enhancement. Current methodologies, however, often overlook exposure errors during the fusion process, potentially compromising the quality of the outcomes. To tackle this challenge, we introduce a novel method titled “An Infrared and Visible Light Fusion Method for Enhanced Representation of Prominent Targets and Exposure Errors” (PEFusion). This approach incorporates a multi-scale exposure correction module to refine image quality in the presence of exposure discrepancies. Furthermore, we implement targeted enhancements during the fusion process. This method facilitates the creation of fused images that not only highlight prominent targets but also maintain high overall quality. Experimental results validate that PEFusion surpasses contemporary state-of-the-art methods in both quantitative and qualitative evaluations. The integration of exposure correction and target enhancement substantially enriches the texture and visual details of the merged images, rendering it exceptionally effective for sophisticated visual tasks.

Bornean Orangutan Nest Classification using Image Enhancement with Convolutional Neural Network and Kernel Multi Support Vector Machine Classifier

Preserving wildlife habitats is crucial in mitigating climate change. Species like orangutans and monkeys contribute to fruiting and planting in forests. The World Wide Fund Sabah Malaysia faces challenges in manually identifying and classifying orangutan nests for studying their behaviour and conserving their habitats. To address this, we propose automating the classification of captured images using machine learning algorithms. This research involves three key components: image processing, feature extraction, and image classification. Our proposed image processing includes several steps, such as image pre-processing and enhancement techniques like local contrast enhancement, sharpening, intensity adjustment, histogram equalization, and colour thresholding. We applied four different Convolutional Neural Networks (CNNs) to extract and identify orangutan nests’ features. Subsequently, we utilize Support Vector Machine (SVM) for image classification. The results reveal that the Inception Residual Network Version 2 (ResNet-v2) achieves the best performance. This architecture is then combined with a kernel SVM to classify Bornean orangutan nests. Our approach demonstrates impressive results, boasting an accuracy of 96.60%, an F1-score of 96.60%, a precision of 96.59%, and a recall of 96.58%. These metrics underscore the high accuracy and effectiveness of our proposed methodology for classifying Bornean orangutan nests. By reducing the need for extensive human intervention in image analysis, our method presents a valuable tool for conservationists and researchers committed to studying and safeguarding these endangered orangutans and their habitats. In future work, we aim to develop orangutan nest detector, contributing to wildlife conservation research.

A novel underwater object detection enhanced algorithm based on YOLOv5‐MH

AbstractUnderwater object detection is an important application of underwater vehicles. However, traditional underwater object detection algorithms have several shortcomings in underwater settings. These include imprecise feature extraction, slow detection speeds, and a lack of robustness. To address these shortcomings, a novel approach based on YOLOv5‐MH (Multi‐Head) is proposed in this paper. Firstly, an image enhancement technique is utilised. This technique uses adaptive linear mapping to adjust contrast and improve the quality of underwater images. Secondly, the C2f module for feature extraction is employed to enable more effective capture of object characteristics. Subsequently, the multi‐head self‐attention and coordinate attention are integrated into the network's backbone. This integration increases the attention given to input data, thereby enhancing the network's performance in handling complex tasks. Furthermore, a bidirectional feature pyramid is implemented to adeptly handle objects of varying scales and sizes, and elevate model performance. Finally, through comprehensive testing on the 2018 URPC dataset and deep plastic dataset, this method demonstrates superior performance. This performance is compared to the original YOLOv5 and other similar networks. It holds immense promise for practical applications across a wide spectrum of underwater tasks.

Understanding the Activity Performance of Comets 38P/Stephan-Oterma, 64P/Swift-Gehrels and C/2017 M4 (ATLAS) through Broadband Photometric Observations

In this work, we report observations of three comets: 38P/Stephan-Oterma, 64P/Swift-Gehrels, and C/2017 M4 (ATLAS), conducted with the Nanshan one-meter wide-field telescope in 2018 August and November, and 2019 January. We extracted morphological features through image enhancement techniques and calculated the dust activity parameter, Afρ, along with dust mass loss rates and coma color indices using broadband photometric data. Our morphological analysis uncovered a spectrum of dust characteristics among the observed comets, ranging from a significant twisted structure in comet 38P/Stephan-Oterma’s coma to the regular coma envelope surrounding comet 64P/Swift-Gehrels. The Af ρ values varied between 148.8 ± 0.3 cm for 64P/Swift-Gehrels and 1118.5 ± 6.2 cm for C/2017 M4 (ATLAS) (measured within a reference aperture radius of ρ = 6″), indicating a range from moderate to high activity levels. Dust mass loss rates were estimated from 328.1 kg s−1 for comet 64P/Swift-Gehrels to 1395.5 kg s−1 for comet C/2017 M4 (ATLAS). The color indices of comets 38P/Stephan-Oterma and C/2017 M4 (ATLAS) closely resemble the average colors of active short-period comets and active long-period comets, respectively. In contrast, 64P/Swift-Gehrels exhibits a significantly bluer hue than typical Jupiter family comets.

Digital Medical Images Segmentation by Active Contour Model based on the Signed Pressure Force Function

The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model.

Denoising diffusion post-processing for low-light image enhancement

Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. Furthermore, each particular LLIE approach may introduce a different form of flaw within its enhanced results. To combat these image degradations, post-processing denoisers have widely been used, which often yield oversmoothed results lacking detail. We propose using a diffusion model as a post-processing approach, and we introduce Low-light Post-processing Diffusion Model (LPDM) in order to model the conditional distribution between under-exposed and normally-exposed images. We apply LPDM in a manner which avoids the computationally expensive generative reverse process of typical diffusion models, and post-process images in one pass through LPDM. Extensive experiments demonstrate that our approach outperforms competing post-processing denoisers by increasing the perceptual quality of enhanced low-light images on a variety of challenging low-light datasets. Source code is available at https://github.com/savvaki/LPDM.

Fusion of transformer attention and CNN features for skin cancer detection

Melanoma is a skin cancer that spreads quickly and has serious risks. Early diagnosis is essential, but since the symptoms of skin lesions in the early stages are vague and similar, they can be difficult for specialists to detect. Therefore, machine learning-based alternative diagnostic methods can be developed in addition to existing ones. This study proposes a new deep learning model, a modified lightweight vision transformer (ViT) architecture, and a hybrid framework developed with an integrated deep learning model and an Ensemble Learning (EL) model for the early-stage diagnosis of skin lesions. The proposed deep learning model was developed based on convolution layers and transformers. The model is called multi-head attention block depthwise separable convolution network (MABSCNET). The proposed hybrid framework was developed by combining modern deep learning and EL models pre-trained with the ImageNet dataset along with the MABSCNET model. In the experimental process, the effectiveness of the proposed methods was evaluated on the ISIC 2020 dataset. Additionally, additional experiments were conducted on ISIC 2018 and a Kaggle dataset to analyze the proposed hybrid framework's classification performance. Image enhancement techniques were used in the datasets. In the ISIC 2020 dataset, the MABSCNET model reached 78.63 % accuracy, the ViT model obtained 76.50 %, and the hybrid framework reached 92.74 % accuracy. Moreover, the proposed hybrid framework achieved 100 % on the ISIC 2018 dataset and 94.24 % on the Kaggle dataset.

Advancements in low light image enhancement techniques and recent applications

Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to those taken in daylight, resulting in occluded images characterized by distortion, low contrast, low brightness, a narrow gray range, and noise. Low-light image enhancement techniques play a crucial role in enhancing the effectiveness of object detection. This paper reviews state-of-the-art low-light image enhancement techniques and their developments in recent years. Techniques such as gray transformation, histogram equalization, defogging, Retinex, image fusion, and wavelet transformation are examined, focusing on their working principles and assessing their ability to improve image quality. Further discussion addresses the contributions of deep learning and cognitive approaches, including attention mechanisms and adversarial methods, to image enhancement.

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier.

Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established. We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering. The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes. According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1. K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

PheoSeg: A 3D transfer learning framework for accurate abdominal CT pheochromocytoma segmentation and surgical grade prediction

Pheochromocytoma is a rare urological disease. Accurate segmentation of pheochromocytoma and its surrounding abdominal organs is crucial for surgical planning and preoperative diagnosis. However, current clinical datasets typically include tumor labels but lack comprehensive multi-organ abdominal labels, and detailed descriptions of the spatial relationships between the tumor and adjacent organs are insufficient. To address the challenge of automatic multi-organ segmentation in the abdomen without comprehensive labels around pheochromocytoma, we propose a novel method for automatic multi-source migration segmentation and preoperative diagnostic grading of pheochromocytoma and its surrounding organs. This method utilizes nnUNet to extract data fingerprint information from the target domain (Clinical Pheochromocytoma dataset of Peking Union Medical College Hospital, PPGL) as prior knowledge, which is then transferred to two source domains (FLARE2022 and AMOS2022). We trained 10 models and incorporated them into a "Model Bank". Additionally, we employed an improved CC-FV method to screen the models in the "Model Bank," identifying the model with the highest mobility (class consistency and feature diversity). This selected model served as a pre-trained model for abdominal multi-organ segmentation and was subsequently adapted for tumor segmentation. To address the issue of limited clinical data, we introduced a data enhancement technique for 3D medical images. The enhanced dataset was used to fine-tune the pre-trained model, resulting in a model capable of segmenting multiple organs and tumors in the abdomen. From the segmentation results, we extracted the spatial relationships between the tumor and surrounding abdominal organs and constructed a radiomic model to predict and analyze the surgical grading. Experimental results demonstrate that our proposed method achieves a Dice coefficient that is, on average, 10.18 % higher on FLARE2022 and AMOS2022 datasets compared to segmentation frameworks such as Swin-Unet, UNETR, CoTr, nnformer, and nnUNetv2. For the PPGL dataset, the Dice coefficient for pheochromocytoma and surrounding organs was 87.27 %. In summary, our method effectively realizes efficient and automatic segmentation of pheochromocytoma and surrounding organs, as well as preoperative diagnostic classification.

An Improved Image Enhancement Technique for Low Light Images Using Deep Learning Approach

Image enhancement in facial detection is a critical component of facial recognition systems. Face identification in an uncontrolled environment is affected by a multitude of difficulties such as poor light levels, low-resolution cameras, occlusions from surrounding objects, and tiny faces in distant photographs. Low signal-to-noise ratio, low brightness, and noise in low-light photographs lead to issues such as color distortion and poor visibility, which makes it challenging to identify faces. Many techniques to enhance low-light images have been developed, improving the face detection system’s accuracy. This will improve the picture at the expense of higher running costs and lower model robustness. The proposed technique, DCE-Net, uses performance-intensive deep learning and light-enhanced image properties. A non-referential deep learning technique was employed to acquire and modify the image attributes. A set of loss functions designed to perform without ground-truth images is the foundation of the deep network learning employed. Compared to the current referential methods, straightforward non-referential light estimation curve mapping minimizes the computational demand for low-light image improvement. Several experiments conducted on standard datasets demonstrated the efficacy and reduced computational requirements of the approach. The effectiveness of this method is supported by both the qualitative and quantitative outcomes. The PSNR and SSIM computation for paired images shows promising results using the proposed image enhancement technique.

Enhancements in image quality and block detection performance for Reinforced Soil-Retaining Walls under various illuminance conditions

To ensure continuous monitoring of reinforced soil-retaining walls (RSWs) even under low-illuminance conditions, such as during the night, it is imperative to evaluate the performance of deep learning-based detection. In this study, we constructed a laboratory RSW model and generated a dataset with varying illuminance levels to assess the impact of image enhancement and block detection. Various image enhancement methods were applied to improve image quality and evaluate their effect on deep learning. RGB optimization (RO) was proposed to optimize RGB intensity and compared with gamma correction, histogram equalization, and low-light image enhancement with illumination map estimation. RO demonstrated outstanding image enhancement performance, as evidenced by lightness order error, peak signal-to-noise ratio, and structural similarity index measure, ensuring high image quality. The trained RO model using Mask R-CNN exhibited excellent accuracy, recall, and F1 score, delivering remarkable detection performance under low illuminance conditions, resulting in a 7.44 % improvement in the F1 score. Image enhancement techniques that maintain similarity, such as lightness order error and structural similarity, across varying illuminance conditions contribute to enhancing the block detection performance of Mask R-CNNs.

Robust vision detection of pipeline solder joints

PurposeSolder joint inspection plays a critical role in various industries, with a focus on integrated chip (IC) solder joints and metal surface welds. However, the detection of tubular solder joints has received relatively less attention. This paper aims to address the challenges of detecting small targets and complex environments by proposing a robust visual detection method for pipeline solder joints. The method is characterized by its simplicity, cost-effectiveness and ease of implementation.Design/methodology/approachA robust visual detection method based on the characteristics of pipeline solder joints is proposed. With the improved hue, saturation and value (HSV) color space, the method uses a multi-level template matching approach to first segment the pipeline from the background, and then match the endpoint of the pipeline to accurately locate the solder joint. The proposed method leverages the distinctive characteristics of pipeline solder joints and employs an enhanced HSV color space. A multi-level template matching approach is utilized to segment the pipeline from the background and accurately locate the solder joint by matching the pipeline endpoint.FindingsThe experimental results demonstrate the effectiveness of the proposed solder joint detection method in practical detection tasks. The average precision of pipeline weld joint localization exceeds 95%, while the average recall is greater than 90%. These findings highlight the applicability of the method to pipeline solder joint detection tasks, specifically in the context of production lines for refrigeration equipment.Research limitations/implicationsThe precision of the method is influenced by the placement angle and lighting conditions of the test specimen, which may pose challenges and impact the algorithm's performance. Potential avenues for improvement include exploring deep learning methods, incorporating additional features and contextual information for localization, and utilizing advanced image enhancement techniques to improve image quality.Originality/valueThe proposed pipeline solder joint detection method offers a novel and practical approach. The simplicity, cost-effectiveness and ease of implementation make it an attractive choice for detecting pipeline solder joints in different industrial applications.

Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate classification, and efficient treatment approaches are essential for addressing this critical health issue. Common side effects include abdominal pain, bloating, and discomfort, which can be chronic and debilitating. Nausea and vomiting are also frequent, leading to difficulties in maintaining adequate nutrition and hydration. The current study intends to develop a deep learning (DL)-based approach that automatically classifies GI tract diseases. For the first time, a GastroVision dataset with 8000 images of 27 different GI diseases was utilized in this work to design a computer-aided diagnosis (CAD) system. This study presents a novel lightweight feature extractor with a compact size and minimum number of layers named Parallel Depthwise Separable Convolutional Neural Network (PD-CNN) and a Pearson Correlation Coefficient (PCC) as the feature selector. Furthermore, a robust classifier named the Ensemble Extreme Learning Machine (EELM), combined with pseudo inverse ELM (ELM) and L1 Regularized ELM (RELM), has been proposed to identify diseases more precisely. A hybrid preprocessing technique, including scaling, normalization, and image enhancement techniques such as erosion, CLAHE, sharpening, and Gaussian filtering, are employed to enhance image representation and improve classification performance. The proposed approach consists of twenty-four layers and only 0.815 million parameters with a 9.79 MB model size. The proposed PD-CNN-PCC-EELM extracts essential features, reduces computational overhead, and achieves excellent classification performance on multiclass GI images. The PD-CNN-PCC-EELM achieved the highest precision, recall, f1, accuracy, ROC-AUC, and AUC-PR values of 88.12 ± 0.332 %, 87.75 ± 0.348 %, 87.12 ± 0.324 %, 87.75 %, 98.89 %, and 92 %, respectively, while maintaining a minimum testing time of 0.000001 s. A comparative study utilizes 10-fold cross-validation, ablation study and various state-of-the-art (SOTA) transfer learning (TL) models as feature extractors. Then, the PCC and EELM are integrated with TL to generate predictions, notably in terms of performance and real-time processing capability; the proposed model significantly outperforms the other models. Moreover, various explainable AI (XAI) methods, such as SHAP (Shapley Additive Explanations), heatmap, guided heatmap, Grad-Cam (Gradient-weighted Class Activation Mapping), guided Grad-CAM, and guided Saliency mapping, have been employed to explore the interpretability and decision-making capability of the proposed model. Therefore, the model provides practical intelligence for increasing confidence in diagnosing GI diseases in real-world scenarios.

Enhanced Power Law Transformation for Histopathology Images of Breast Cancer

Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and BreakHis dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images.