Convolutional Encoder Research Articles

Human-like Attention-Driven Saliency Object Estimation in Dynamic Driving Scenes

Identifying a notable object and predicting its importance in front of a vehicle are crucial for automated systems’ risk assessment and decision making. However, current research has rarely exploited the driver’s attentional characteristics. In this study, we propose an attention-driven saliency object estimation (SOE) method that uses the attention intensity of the driver as a criterion for determining the salience and importance of objects. First, we design a driver attention prediction (DAP) network with a 2D-3D mixed convolution encoder–decoder structure. Second, we fuse the DAP network with faster R-CNN and YOLOv4 at the feature level and name them SOE-F and SOE-Y, respectively, using a shared-bottom multi-task learning (MTL) architecture. By transferring the spatial features onto the time axis, we are able to eliminate the drawback of the bottom features being extracted repeatedly and achieve a uniform image-video input in SOE-F and SOE-Y. Finally, the parameters in SOE-F and SOE-Y are classified into two categories, domain invariant and domain adaptive, and then the domain-adaptive parameters are trained and optimized. The experimental results on the DADA-2000 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in several evaluation metrics and can more accurately predict driver attention. In addition, driven by a human-like attention mechanism, SOE-F and SOE-Y can identify and detect the salience, category, and location of objects, providing risk assessment and a decision basis for autonomous driving systems.

Detection and Classification of Skin Cancer Using Unmanned Transfer Learning Based Probabilistic Multi-Layer Dense Networks

Skin cancer is one of the most dangerous cancers that may occur for different age groups of people. As a result, early identification of skin cancer has the potential to save millions of lives. In Traditional machine learning approaches, there are various drawbacks in detection and classification of skin lesions. As a result, to achieve the robust performance, initially the joint trilateral and bilateral filter (JTBF) with convolutional auto encoder and decoder (CAED)-based preprocessing method is used to enhance the skin lesion and also removes hair from lesions. Then, transfer learning-based probabilistic multi-layer dense networks (PMDN) method-based unmanned Transfer learning segmentation method is adapted for accurately detecting the cancer region on skin lesions. Further, transfer learning convolution neural network (TL-CNN) is used to extract the features from the segmented region, which extracts the detailed inter-disease-dependent (IDD) and intra-disease specific (IDS) features. Finally, Alexa Net model is trained and tested with the IDD, IDS features and classifies the eight different skin cancer types. The complexity of the transfer learning networks is optimized by the using the Adam optimizer. Finally, the simulation results show that the proposed model resulted in superior segmentation, feature extraction, and classification performances as compared to conventional approaches. Further, the proposed method achieved 99.937% segmentation accuracy, 99.47% feature extraction accuracy, and 99.27% classification accuracy on ISIC-2019 public challenge dataset.

A Soft-GJETP Based Blind Identification Algorithm for Convolutional Encoder Parameters

In an adaptive or non-cooperative communication system, the blind identification of channel coding is an indispensable procedure for recovering the message from the intercepted coding data. Due to the wide applications of convolutional codes, the blind identification problem of convolutional codes has also received extensive studies. In this paper, we consider the blind identification problem of convolution encoder parameters in the general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k/n$ </tex-math></inline-formula> rate case. To improve the blind identification accuracy of existing hard decision based solutions, a novel soft information based blind identification algorithm is designed in this paper. Specifically, a Soft Gaussian-Jordan Elimination Through Pivoting (Soft-GJETP) algorithm is firstly proposed to calculate the rank of the received data matrix. In contrast to the existing hard decision based GJETP algorithm, this Soft-GJETP algorithm formulates the soft information of the received data, then defines three reliability metrics and derives updating rules for this soft information. In this way, the diagonals of the received data matrix have fewer errors in Soft-GJETP algorithm. Furthermore, a soft decision strategy is proposed with a weight-dependent threshold. Employing this strategy, the dependent columns can be distinguished with higher accuracy. Finally, simulation results and comparisons are given to illustrate the performances of our proposed methods.

A novel method for identifying recursive systematic convolutional encoders based on the cuckoo search algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively. With the increase of the codeword length and constraint length, the search space expands exponentially, and thus it limits the application of these methods in practice. To overcome the limitation, a novel identification method, which gets rid of exhaustive test, is proposed based on the cuckoo search algorithm by using soft-decision data. Firstly, by using soft-decision data, the probability that a parity check equation holds is derived. Thus, solving the parity check equations is converted to maximize the joint probability that parity check equations hold. Secondly, based on the standard cuckoo search algorithm, the established cost function is optimized. According to the final solution of the optimization problem, the generator matrix of recursive systematic convolutional code is estimated. Compared with the existing methods, our proposed method does not need to search for the generator matrix exhaustively and has high robustness. Additionally, it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

Trajectory-as-a-Sequence: A novel travel mode identification framework

Identifying travel modes from GPS tracks, as an essential technique to understand the travel behavior of a population, has received widespread interest over the past decade. While most previous Travel Mode Identification (TMI) methods separately identify the mode of each track segment of a GPS trajectory, in this paper, we propose a sequence-based TMI framework that constructs a feature sequence for each GPS trajectory and sent it to a sequence-to-sequence (seq2seq) model to obtain the corresponding travel mode label sequence, named Trajectory-as-a-Sequence (TaaS). The proposed seq2seq model consists of a Convolutional Encoder (CE) and a Recurrent Conditional Random Field (RCRF), where the CE extracts high-level features from the point-level trajectory features and the RCRF learns the context information of trajectories at both feature and label levels, thus outputting accurate and reasonable travel mode label sequences. To alleviate the lack of data, we adopted a two-stage model training strategy. Additionally, we design two novel bus-related features to assist the seq2seq model distinguishing different high-speed travel modes (i.e., bus, car, and railway) in the sequence. Besides the classical performance metrics such as accuracy, we propose a new metric that evaluates the rationality of the travel mode label sequence at the trajectory level. Comprehensive evaluations corresponding to the real-world TMI applications show that the sequence-based TaaS outperforms the segment-based models in practice. Furthermore, the results of ablation studies demonstrate that the elements integrated into the TaaS framework are helpful to improve the efficiency and accuracy of TMI.

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.

Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios: Scenario A (Binary classification) and Scenario B (Multi-classification). Data collection and preprocessing, patch image creation and splitting, and artificial intelligence-based breast lesion identification are all components of the execution framework that are applied consistently across both cases. The effectiveness of the proposed AI model is compared against three separate deep learning models: a custom CNN, the VGG16, and the ResNet50. Two datasets, CBIS-DDSM and DDSM, are utilized to construct and test the proposed CAD system. Five-fold cross validation of the test data is used to evaluate the accuracy of the performance results. The suggested hybrid CAD system achieves encouraging evaluation results, with overall accuracies of 100% and 95.80% for binary and multiclass prediction challenges, respectively. The experimental results reveal that the proposed hybrid AI model could identify benign and malignant breast tissues significantly, which is important for radiologists to recommend further investigation of abnormal mammograms and provide the optimal treatment plan.

Research on Network Intrusion Detection Based on an Improved Deep Learning Method

Network intrusion detection is an important research direction in the field of network security. The traditional detection algorithm is based on feature extraction and feature separation, which has the problems of low detection accuracy and high false alarm rate. In order to improve the accuracy of network intrusion detection, this paper proposes an intrusion detection model based on deep asymmetric convolutional encoder and Random Forest(RF). First, use DACAE to extract features from the preprocessed data, and then use the random forest algorithm to divide the network traffic data into normal and abnormal classes, and finally achieve the purpose of network intrusion detection. It is tested on three public benchmark datasets of network intrusion detection NSL-KDD and KDD99 datasets. The experimental results show that the accuracy and false alarm rate of the improved method are better than the comparative method.

RFMic-Phone: Robust Sound Acquisition Combining Millimeter-Wave Radar and Microphone

With the increasing popularity of the Internet of Things and smart factories, robust sound acquisition plays an important role in communication and human–machine interaction, for example, in-vehicle voice control interactions, smart home, and robotic voice interaction. However, due to the inherent sensing manners of the widely used microphone system, the existing audio-only sound acquisition system has fundamental limitations in solving the cocktail problem and is difficult to adapt to complex acoustic environments. Considering the advantages of millimeter-wave (mmWave) radar in the field of target positioning and precise vibration measurement, the mmWave vibration measurement-based sound perception has great potential to solve the above problems. However, there are still difficulties in high-quality sound recovery. In this letter, we propose RFMic-Phone, a robust sound acquisition system combining a mmWave radar and a traditional microphone, which combines the sound signals captured by the mmWave radar and microphone, offering a novel approach for reliable sound acquisition in the complex acoustic environment. The microphone is used to obtain the mixed and high-fidelity audio signals, while the radar is used to obtain the vibration of the sound source. To achieve intelligent and effective feature fusion, we employ a deep learning framework with a modified convolutional encoder–decoder neural network structure. Moreover, we propose to utilize the real and imaginary spectra of the target sound source as the input of the network, allowing achieve high-quality target sound signals. The experimental results show that the RFMic-Phone can achieve robust and high-quality sound signals acquisition in a variety of complex acoustic environments.

Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation.

Recent deep learning frameworks draw strong research interest in application of ego-motion estimation as they demonstrate a superior result compared to geometric approaches. However, due to the lack of multimodal datasets, most of these studies primarily focused on single-sensor-based estimation. To overcome this challenge, we collect a unique multimodal dataset named LboroAV2 using multiple sensors, including camera, light detecting and ranging (LiDAR), ultrasound, e-compass and rotary encoder. We also propose an end-to-end deep learning architecture for fusion of RGB images and LiDAR laser scan data for odometry application. The proposed method contains a convolutional encoder, a compressed representation and a recurrent neural network. Besides feature extraction and outlier rejection, the convolutional encoder produces a compressed representation, which is used to visualise the network’s learning process and to pass useful sequential information. The recurrent neural network uses this compressed sequential data to learn the relationship between consecutive time steps. We use the Loughborough autonomous vehicle (LboroAV2) and the Karlsruhe Institute of Technology and Toyota Institute (KITTI) Visual Odometry (VO) datasets to experiment and evaluate our results. In addition to visualising the network’s learning process, our approach provides superior results compared to other similar methods. The code for the proposed architecture is released in GitHub and accessible publicly.

Segmentation of shoulder muscle MRI using a new Region and Edge based Deep Auto-Encoder

Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operations in the Convolutional encoder and decoder blocks. The DAE’s region and edge-based segmentation encourage the network to extract homogenous and anatomical information, respectively. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps effectively learn the tear region. The performances of the proposed RE-DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58% and 87.07%, an accuracy of 81.57% and 95.58% for tear and muscle regions, respectively. The high visual quality and the objective result suggest that the proposed SA-RE-DAE can correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions.

Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction

Motivation: Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. Methods:In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC’s convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. Results:We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models’ capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.

Complexity reduction over Bi-RNN-based nonlinearity mitigation in dual-pol fiber-optic communications via a CRNN-based approach

Bidirectional recurrent neural networks (bi-RNNs), in particular bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models, have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a unidirectional many-to-one vanilla RNN operate in tandem, each best capturing one set of channel impairments while compensating for the shortcomings of the other. We deploy this model in two different receiver configurations. In one, the neural network is placed after a linear equalization chain and is merely responsible for nonlinearity mitigation; in the other, the neural network is directly placed after the chromatic dispersion compensation and is responsible for joint nonlinearity and polarization mode dispersion compensation. For a 16-QAM 64 GBd dual-polarization optical transmission over 14×80km standard single-mode fiber, we demonstrate that the proposed hybrid model achieves the bit error probability of the state-of-the-art bi-RNN-based methods with greater than 50% lower complexity, in both receiver configurations.

Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations

Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological subpopulations, requires some type of image analysis method. Suggested methods to classify particles have been based on pre-determined morphological features or use supervised training of convolutional neural networks to learn image representations in relation to ground truth labels. Complications arising from highly complex morphologies, unforeseen classes, and time-consuming preparation of ground truth labels, are some of the challenges faced by these methods. In this work, we evaluate the application of a self-supervised contrastive learning method in studying particle images from therapeutic solutions. Unlike with supervised training, this approach does not require ground truth labels and representations are learned by comparing particle images and their augmentations. This method provides a fast and easily implementable tool of coarse screening for morphological attribute assessment. Furthermore, our analysis shows that in cases with relatively balanced datasets, a small subset of an image dataset is sufficient to train a convolutional neural network encoder capable of extracting useful image representations. It is also demonstrated that particle classes typically observed in protein solutions administered by pre-filled syringes emerge as separated clusters in the encoder's embedding space, facilitating performing tasks such as training weakly-supervised classifiers or identifying the presence of new subpopulations.

Vision Transformer with Convolutional Encoder–Decoder for Hand Gesture Recognition using 24-GHz Doppler Radar

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.

Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

We propose a framework for saliency-based, multi-target detection and segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our framework relies on a multi-branch, convolutional encoder-decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a salient target. These opinions are aggregated and cascaded into a deep-parsing network to refine the segmentation. We evaluate our framework using real-world CSAS imagery consisting of five broad target classes. We compare against existing approaches from the computer-vision literature. We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery. It greatly outperforms unsupervised saliency approaches developed for natural imagery. This illustrates that natural-image-based models may need to be altered to be effective for this imaging-sonar modality.

A double serial concatenated code using CRC-aided error correction for highly reliable communication

Highly Reliable Communication (HRC) is an emerging paradigm for meeting the quality of service requirements of new applications such as intelligent vehicle networking and industrial Internet. HRC brings challenges to channel coding and multiple access. Various channel coding techniques have been proposed to improve reliability, but often suffer from limited reliability and high decoding complexity. This work presents a double serial concatenated code using CRC-aided error correction, abbreviated as DSC 3 EC , to improve reliability and reduce complexity. The encoding scheme is a double serial concatenated code, with the cascade of a set of CRC encoders, a linear block encoder, a series of interleavers , and a set of convolutional encoders . The decoding scheme consists of two modules: (i) decoding of serial concatenated codes, which includes the decoding of single parity check convolutional code (SPC-CC) and the decoding of Reed–Solomon convolutional code (RS-CC) for intra-block error correction at the physical layer to control BER; (ii) decoding of CRC-aided linear error correction, which can effectively identify, locate and correct error bits at the inter-block level to solve the problem of block loss caused by residual errors at the physical layer. Simulation results show that the joint scheme can effectively improve communication reliability while reducing complexity.

Low-Power VLSI Implementation of Novel Hybrid Adaptive Variable-Rate and Recursive Systematic Convolutional Encoder for Resource Constrained Wireless Communication Systems

In the modern wireless communication system, digital technology has tremendous growth, and all the communication channels are slowly moving towards digital form. Wireless communication has to provide the reliable and efficient transfer of information between transmitter and receiver over a wireless channel. The channel coding technique is the best practical approach to delivering reliable communication for the end-users. Many conventional encoder and decoder units are used as error detection and correction codes in the digital communication system to overcome the multiple transient errors. The proposed convolutional encoder consists of both Recursive Systematic Convolutional (RSC) Encoder and Adaptive Variable-Rate Convolutional (AVRC) encoder. Adaptive Variable-Rate Convolutional encoder improves the bit error rate performance and is more suitable for a power-constrained wireless system to transfer the data. Recursive Systematic Convolutional encoder also reduces the bit error rate and improves the throughput by employing the trellis termination strategy. Here, AVRC encoder ultimately acquires the channel state information and feeds the data into a fixed rate convolutional encoder and rate adaptor followed by a buffer device. A hybrid encoder combines the AVRC encoder and RSC encoder output serially and parallel, producing the solid encoded data for the modulator in the communication system. A modified turbo code is also obtained by placing interleaver between the two encoder units and building the stronger code word for the system. Finally, the conventional encoder system is compared and analyzed with the proposed method regarding the number of LUT’s, gates, clock cycle, slices, area, power, bit error rate, and throughput.

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT). We input pre-therapy free breathing (FB) computed tomography (CT) images from 849 patients treated with lung SBRT into a multitask deep neural network to generate an image fingerprint signature (or DL score) that predicts time-to-event local failure outcomes. The network includes a convolutional neural network encoder for extracting imaging features and building a task-specific fingerprint, a decoder for estimating handcrafted radiomic features, and a task-specific network for generating image signature for radiotherapy outcome prediction. The impact of tumor motion on the DL scores was then examined for a holdout set of 468 images from 39 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images. Tumor motion was estimated using a 3D vector of the maximum distance traveled, and its association with DL score variance was assessed by linear regression. The variance and amplitude in 4D CT image-derived DL scores were associated with tumor motion (R2 =0.48 and 0.46, respectively). Specifically, DL score variance was deterministic and represented by sinusoidal undulations in phase with the respiratory cycle. DL scores, but not tumor volumes, peaked near end-exhalation. The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r=0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p<0.0001). An image-based DL risk score derived from a series of 4D CT images varies in a deterministic, sinusoidal trajectory in a phase with the respiratory cycle. These results indicate that DL models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.

Simulation and prediction of three-dimensional rotating flows based on convolutional neural networks

Two deep learning models to reconstruct three-dimensional (3D) steady-state rotating flows are proposed to capture the spatial information: the 3D convolutional encoder–decoder and the 3D convolutional long short-term memory model. They are based on deep learning methods such as the encoder–decoder convolutional neural network and recurrent neural network. Their common components are an encoder, a middle layer, and a decoder. The rotating flows in a stirred tank with four inclined blades are calculated for the dataset to train and test the two models. A workflow for the flow field reconstruction is established and all variants made up of various components are executed according to the flow. The optimal networks of the two models are selected by comparing performance measures. The results show that both models have the excellent ability to fit the 3D rotating flow field. Performance measures of the second model are better than those of the first one, but its running time is slower than that of the first one. In practice, this method can be used in the design and optimization of stirred tanks, centrifugal pumps, and other machines with rotating parts.

Hybrid convolution network for medical images processing and breast cancer detection

In this paper, the breast cancer detection problem using convolutional neural networks (CNN) is considered. The review of known works in this field is presented and analysed. Most of them rely only on feature extraction after the convolutions and use the precision of classification of malignant tumors as the main criterion. However, because of the huge number of parameters in the models, the time of computation is very large. A new structure of CNN is developed — a hybrid convolutional network consisting of convolutional encoder for features extraction and reduction of the complexity of the model and CNN for classification of tumors. As a result, it prevented overfitting the model and reduced training time. Further, while evaluating the performance of the convolutional model, it was suggested to consider recall and precision criteria instead of only accuracy like other works. The investigations of the suggested hybrid CNN were performed and compared with known results. After experiments, it was established the proposed hybrid convolutional network has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60%, and 93%, respectively, and requires much less training time in the problem of breast cancer detection as compared with known works.