Breast cancer is a prevalent and life-threatening disease where early and accurate diagnosis is critical for effective treatment. Conventional histopathological analysis, while the standard for diagnosis, can be laborious and is subject to inter-observer variability, highlighting the need for robust automated methods. This article introduces EVC-Net, a novel hybrid deep learning framework designed to automate the classification of breast cancer from histopathological images. EVC-Net synergistically integrates an EfficientNetV2S for fine-grained texture feature extraction, a vision transformer (ViT) for capturing global context, and a capsule network to preserve spatial hierarchies within tissue structures. The proposed model is evaluated on the public BreakHis dataset. Across all four magnification levels, EVC-Net demonstrates robust performance, achieving an average accuracy of 0.985 and an AUC-ROC of 0.994 for binary (benign vs. malignant) classification. For the eight-subtype multi-class task, the model maintains high efficacy, attaining an average accuracy of 0.954 and an AUC-ROC of 0.980. Furthermore, interpretability analysis using Grad-CAM is conducted, generating heatmaps overlay visualization to understand the rational behind model’s predictions. These results demonstrate the potential of the EVC-Net framework to enhance diagnostic accuracy and consistency, offering valuable support for clinical workflows in oncology.

Accurate multi-class classification of skin lesions remains a challenging task due to the intrinsic complexity of dermoscopic images and the severe class imbalance present in medical datasets. This paper introduces an ensemble-based deep learning pipeline designed to achieve robust and generalizable multi-class skin lesion diagnosis. The proposed framework systematically addresses the primary challenges of class imbalance using an aggressive mix up augmentation. A five face structured pipeline encompassing data preparation, stratified crossvalidation, model training, ensemble inference, and performance evaluation is performed. The proposed ensemble integrates five heterogeneous deep architectures: Vision Transformer (ViT), Swin Transformer, ConvNeXt, EfficientNet, and DenseNet, each contributing complementary representational strengths. To enhance model robustness and fairness, the pipeline incorporates regularization and a hybrid loss combining focal Loss and label smoothing. Ensemble predictions are aggregated using a weighted soft voting strategy, ensuring stable and accurate classification outcomes across all lesion categories. Experimental evaluations demonstrate that the proposed system achieves state-of-the-art performance on the HAM10000 dataset, delivering high accuracy and balanced sensitivity across minority classes. The end-to-end design emphasizes reproducibility, scalability, and transparency, establishing a robust foundation for future research in automated dermatological diagnosis.

Numerous life-sustaining processes such as transcription, replication, and splicing, regulate the biological systems in a complex way. Therefore, it is important to recognize the pivotal role of crucial protein and DNA interactions for understand the above life sustaining processes. Hence, Protein-DNA binding residues is essential to identify as mentioned above. However, the classical experimental methods are tedious and labor-intensive. The recent research address these challenges and focused on predicting binding residues from sequence data. This present model utilizes a protein language model to transform protein sequence data into vector formats, enabling efficient computation. The model comprises three main components: an embedding layer that provide conversion of protein sequences into continuous vectors in the form of feature embedding using Protein-Bert model and a four-layer residual based convolutional neural network (CNN) trained on these vectors for prediction. The Binary Cross Entropy and contrastive loss function is used for loss computation. The same model, then reformatted, could be used to predict binding residues of RNA and antibodies. the present model achieves 0.884 AUC score and 0.795 specificity as compare to previous models.

Nowadays, the demand for the Industrial Internet of Things (IIoT) technology has increased immensely in various fields, such as the agriculture industry, smart mines, smart factories, healthcare industry, etc. Industrial wireless sensor networks (IWSNs) act as a backbone of any IIoT system by forming a network of heterogeneous sensors. In IWSNs, the fault occurrence probability is more due to continuous exposure to harsh environments. Furthermore, the presence of obstacles creates an extra burden in fault detection. In this article, the proposed scheme presents an optimal fault diagnostic point selection mechanism that significantly reduces fault detection latency and energy consumption. Multiple intelligent mobile fault detectors effectively avoid obstacles during the fault detection process that significantly improves fault detection accuracy (FDA). Extensive simulations and a testbed experiment demonstrate the effectiveness of the proposed scheme in terms of FDA, false alarm rate, false positive rate, F1-score, energy consumption, and network lifetime.

Extraction from large no. of features (genes signatures) are the major issues in the prediction of cancer and its specific type identification using microarray datasets. Even though most classifiers predict the class (normal or cancerous) for various cancers, the accuracy of prediction still suffers. This is due to the importance of fewer gene signatures for a particular cancer and classification of samples independent from their originating form. The present paper proposes gene extraction techniques to work in an unsupervised manner. The proposed technique takes the advantage of both linear and non-linear feature extraction methods. Principal component analysis (PCA) is used in a linear manner whereas Denoising Autoencoder (DAE) is used in a nonlinear manner. In the first phase of the work, feature space extracts from both the methods have been combined and new features space has been utilized for cancer classification. Here, Four classifiers: Support vector machine (SVM), Multilayer perceptron (MLP), Naive Bays (NB) and Decision Tree are applied on a no. of gene signatures extracted from four different cancer datasets. It is seen that after feature extraction from this PCA-DAE, classification accuracy either increases or attains its maximum value in comparison with base techniques except NB.

Recently, the Internet of Things (IoT) has attracted much interest in its wide applications, such as smart healthcare, home automation, transportation, and smart city. In these IoT-based systems, wireless sensor networks (WSNs) are highly used to gather information needed by smart environments. However, due to huge heterogeneous data coming from different sensing devices, IoT-enabled WSNs face different challenges, such as high communication delay, low throughput, and poor network lifetime. In this article, a deep-reinforcement-learning (DRL)-based intelligent routing scheme is proposed for IoT-enabled WSNs that significantly reduce delay and increase network lifetime. The proposed algorithm divides the whole network into different unequal clusters depending on the current data load present in the sensor node that significantly prevents immature death of the network. An extensive experiment on the proposed algorithm is performed using ns3. The experimental results are compared with the state-of-the-art algorithms to demonstrate the efficiency of the proposed scheme in terms of the number of alive nodes, packet delivery, energy efficiency, and communication delay in the network.

In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size & density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bio-inspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as k-means clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bio-inspired clustering technique known as “Teacher-Learner Based Optimization”. In contrast to most of the well-known clustering techniques, TLBO doesn't require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other well-known clustering techniques.

Breast cancer is one of the major causes of death among women around the world. To diagnose this disease using mammography technique, segmentation is an important step to detect the suspicious region(s) of mammograms. Segmentation concerns to the process of division of mammograms into different sections. Objective of segmentation is to simply modify the presentation of an image so that it becomes more significant and easier to study. Although many algorithms have been proposed yet to segment out the suspicious regions of mammograms, automatic segmentation of masses of improved quality is still considered to be difficult. This study introduces a novel marker controlled watershed algorithm for segmentation of mammograms to highlight the suspicious regions more distinctly. It is a morphological operation on the basis of obtaining watershed lines from a topographic demonstration of the input image. The proposed method has been applied and examined on various difficult to diagnose mammograms taken from MIAS & BIRADS database. Results obtained by this technique are impressive for qualitative analysis and also approved by the radiologists.

This study investigates a novel technique of tissues segmentation of high-grade (HG) glioma. Segmentation of tumor and edema for treatment planning is crucial. Anisotropic diffusion filter removes the noise and preserves the tumor tissues in MRI images. K-mean clustering algorithm clusters the brain tissues in normal and tumor tissues. The healthy tissues surround tumor tissues. Hierarchical centroid Shape descriptor select the tumor tissues and discard the other healthy tissues. The features of multimodality MRI images are fused to segment the tumor and edema. The experiment was carried out on the 200 T1, T1c, T2 and Flair MRI images of 10 high-grade glioma patients. The quantitative evaluation of experiment was carried out over publically available synthetic images from the BRATS2012 database.

In this paper we have presented an automated diagnosis of breast cell cancer using histopathological images on the basis of different textural descriptors. In the proposed technique, the images being preprocessed using extended adaptive-top-bottom transform (EAHE-TBhat) and segmented the nuclei regions from the non-nuclei regions using region growing segmentation. The nuclei regions are then used to extract features and provides texture descriptors using parameter free version of threshold adjacency statistics (PFTAS). The feature vector obtained are then classified as benign tumor feature and malignant tumor features using Rotation Forest (RF) classifier. The proposed technique compared with the other four combination of conventional texture techniques and classifiers. The experimental results and performance metrics values shows that the proposed technique is better than the other conventional techniques.

An innovative and robust image segmentation approach has been proposed for magnetic resonance (MR) brain tumor extraction. We have proposed a novel technique to classify a given MR brain image as benign or malignant. In order to extract the features from given MR brain tumor image, we have first employed wavelet transform which is then followed by Laplacian Eigen maps (LE) so as to curtail the dimensions of extracted features. These reduced features are now given to kernel support vector machine (K-SVM). Once we are done with classification, the next logical step remains image segmentation. We have the proposed algorithm with Gaussian Radial Basis (GRB) kernel owing to the fact that it achieves higher efficiency. Moreover, we have adopted the Leave-one-out cross validation (LOOCVCV) strategy so as to enhance generalization of K-SVM. Experimental findings reveal that our proposed algorithm outperformed the existing brain tumor extraction techniques in terms of computational and qualitative aspect. It could serve doctors to examine whether the tumors is benign or malignant.

Current paper represents a new type of encrypted Electronic Patient Record (EPR) code used for data encryption and content protection. EPR is a collection of several private information related to a patient which needs data authenticity, data security as well as safe and secured transmission. The proposed methodology used both cryptography and image processing techniques to build a new type of encrypted information code in image format which can be transmitted and used like bar code, QR code but even more secure. The success rate of recovery of data is 100% for both short and long messages. The information can also be retrieved at the receiver side exactly same without any loss of information. Use of RSA and DES algorithm consequently, with three keys and followed by some image processing techniques like complement, flip make the proposed algorithm more unbreakable. In this paper a complete Graphical User Interface (GUI) has been developed for both encoder-transmitter and decoder- receiver section.

In this paper we have proposed an effective fusion method to combine the information of brain MRI and PET images to study Alzheimer's disease of the patients. The proposed methodology is applied to the wavelet based multi-scale image fusion algorithm. In this method principal component analysis (PCA) approach for integrating each RGB component of approximation images decomposed by multiresolution analysis provides superior visual interpretation and high-quality synthesis of MRI and PET images. The proposed rule overcomes the instability and inconsistency of information occurring in the wavelet based decomposition of images. The experimental results show that this method is very much suitable for multimodal images, especially for integrating structural and functional information of brain MRI and PET data.

Current paper presents a new type of GUI based secure breast cancer identification system through a combination of Cryptography and Biomedical Image Watermarking. Through this scheme 2ndlevel of security can be applied for the embedded data in mammographic images. Several information related to patients (EPR), infected region (ROI), doctor's name and diagnosis from symptoms are encrypted in the 1stlevel using RSA encryption and then embedded in the mammographic image itself in the 2ndlevel using Bit Plane Slicing watermarking technique. The infected region is identified through region growing and contour detection algorithm which needs to be perfect for accurate ROI identification resulting a better treatment. Values of several image quality metrics show that the proposed GUI based breast cancer diagnostic system is good enough for successful recovery of all the embedded information exactly similar. The simplicity and easiness of the complete GUI also makes the entire system user friendly even to the non-medical technical person.

In the Human eye there are two types of image receptor cells cones and rods. Rods are helpful for seeing the image in low light or in the dark whereas the cones work for seeing the image in different colours. Both the receptors cells are found in retina of human eye. They contain three colour pigments Red (Large Bandwidth), Green (Medium Bandwidth) and Blue (Small Bandwidth) by which we are trichromats (tri =3 chromats =colour) and able to see any object in the world because of all objects are viewable by these colour hues. Mostly the colour blindness is the genetic mutation and not to be cured. It can also get place when there is a problem related to pigments in certain nerve cells (cone) of the eye or when there would be a trauma in mind or the chemical accident by which those receptor cells got affect. In the Human vision there are different stages which describe what type of colour vision deficiency the people have. Monochromacy (1%): Total colour blinded Dichromacy (10%): Partial colour blinded (i) red-green colour blindness (ii) blue-yellow colour blindness. Trichromacy: Normal colour vision. The Dichromacy, this type of colour blindness mostly people have. So correcting it, a new filter is developed in the image processing on the basis of Ishihara colour test. It works successfully as per tested it many time. In this filter adjusting, contrast stretching and inverting function are used for providing better result and it does. By applying this new filter the normal and as well colour vision deficient people easily recognise the objects whether they are of any of shape, number or alphabets.

Automatic segmentation of Magnetic Resonance Images (MRI) for tissue classification becomes more challenging when the image is corrupted with noise and intensity non-uniformity (INU). Several fuzzy clustering based statistical retrospective methods exist for simultaneous correction and segmentation of images but most of them fail to be robust in presence of noise, outliers and INU artifacts. In this paper, a hybridization of rough c-means and spatial fuzzy c-means clustering is presented whose objective function has been modified to accommodate INU field as well. While the membership function of fuzzy sets enables efficient handling of overlapping partitions, the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. The experiments conducted on brain MR images show promising results in terms of segmentation accuracy and class separability. The usefulness of proposed algorithm is also investigated on high field MR images. The proposed algorithm Rough-Theoretic Bias-Corrected Fuzzy C-Means Algorithm (R-BCFCM) has significant performance improvement over other similar methods from rough-fuzzy family and can be employed for MRI corrupted with high intensity non-uniformity and noise.

In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs large number of inputs. Simultaneously, to train the network, a large number of training cycles and huge memory are also required. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology genetic algorithm (GA) has been used for the searching of effective input feature vectors. Adaptive neuro-fuzzy model has been used for final classification of different boundaries of tumor masses. The proposed technique is an innovative soft computing approach that removes the limitation of conventional neural networks and indicates a promising direction of adaptation in a changing environment. The classification system utilizes a Euclidean distance function to detect the belongingness of masses in benign and in malignant classes along with degree of benignancy/malignancy. Presently 200 digitized mammograms from MIAS and other databases have been considered for the experiment and which have shown an average of approximately 86% correct classification as compared with clinical data with a highest rate of 88.9%.

An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class definition. For making the segmentation robust to Noise and intensity in-homogeneity, the images are proposed to be pre-processed with a neighbourhood averaging spatial filter. To accelerate the segmentation process, a novel Suppressed Rough Fuzzy C-Means model is presented in which a membership suppression mechanism has been implemented, which creates competition among clusters to speed-up the clustering process. The effectiveness of the presented algorithm along with comparison with other related algorithm has been demonstrated on a set of MR and CT scan images. The results using MRI data show that our method provides better results compared to standard Fuzzy C-Means based algorithms and other modified similar techniques.