Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less than or equal to 5 years. But, it can be cured if detected in early stages. Though there are numerous clinical procedures, the accuracy of diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has been brought into the picture. It helped in increasing the accuracy of diagnosis but could not demolish the error-prone behaviour. A quick and less error-prone solution is needed to diagnose this majorly growing skin cancer. This project deals with the usage of deep learning in skin lesion classification. In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model. Convolution neural networks are known for capturing features of an image. So, they are preferred in analyzing medical images to find the characteristics that drive the model towards success. Techniques like data augmentation for tackling class imbalance, segmentation for focusing on the region of interest and 10-fold cross-validation to make the model robust have been brought into the picture. This project also includes usage of certain preprocessing techniques like brightening the images using piece-wise linear transformation function, grayscale conversion of the image, resize the image. This project throws a set of valuable insights on how the accuracy of the model hikes with the bringing of new input strategies, preprocessing techniques. The best accuracy this model could achieve is 0.886.

皮肤病变的准确诊断是大型皮肤图像中的关键任务。在本研究中，我们形成了一种新型的图像特征，称为混合特征，其具有比单个方法特征更强的辨别能力。本研究涉及一种新技术，在训练过程期间，我们将手工特征或特征传递到完全连接的卷积神经网络（CNN）模型中。根据我们的文献回顾，直到现在，在培训过程中将手工特征注入CNN模型中，没有研究或调查对分类绩效的影响。此外，我们还调查了分割面膜的影响及其对整体分类性能的影响。我们的模型实现了92.3％的平衡式多条准确度，比典型的单一方法为深度学习的单一方法分类器架构优于6.8％。

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.

为了产生最大的影响，必须使用基于证据的决策制定公共卫生计划。创建机器学习算法是为了收集，存储，处理和分析数据以提供知识和指导决策。任何监视系统的关键部分是图像分析。截至最近，计算机视觉和机器学习的社区最终对此感到好奇。这项研究使用各种机器学习和图像处理方法来检测和预测疟疾疾病。在我们的研究中，我们发现了深度学习技术作为具有更广泛适用于疟疾检测的智能工具的潜力，通过协助诊断病情，可以使医生受益。我们研究了针对计算机框架和组织的深度学习的共同限制，计算需要准备数据，准备开销，实时执行和解释能力，并发现对这些限制的轴承的未来询问。

由于其在非洲以外的40多个国家 /地区的迅速传播，最近的蒙基托克斯爆发已成为公共卫生问题。由于与水痘和麻疹的相似之处，蒙基托斯在早期的临床诊断是具有挑战性的。如果不容易获得验证性聚合酶链反应（PCR）测试，那么计算机辅助检测蒙基氧基病变可能对可疑病例的监视和快速鉴定有益。只要有足够的训练示例，深度学习方法在自动检测皮肤病变中有效。但是，截至目前，此类数据集尚未用于猴蛋白酶疾病。在当前的研究中，我们首先开发``Monkeypox皮肤病变数据集（MSLD）。用于增加样本量，并建立了3倍的交叉验证实验。在下一步中，采用了几种预训练的深度学习模型，即VGG-16，Resnet50和InceptionV3用于对Monkeypox和Monkeypox和Monkeypox和其他疾病。还开发了三种型号的合奏。RESNET50达到了82.96美元（\ pm4.57 \％）$的最佳总体准确性，而VGG16和整体系统的准确性达到了81.48美元（\ pm6.87 \％）$和$ 79.26（\ pm1.05 \％）$。还开发了一个原型网络应用程序作为在线蒙基蛋白筛选工具。虽然该有限数据集的初始结果是有希望的，但需要更大的人口统计学多样化的数据集来进一步增强性增强性。这些的普遍性 楷模。

While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities.

Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used to train Deep Learning systems - these have produced impressive results for classification. However, this only works for the classes they are trained on whilst they are incapable of identifying skin lesions from previously unseen classes, making them unconducive for clinical use. We could look to massively increase the datasets by including all possible skin lesions, though this would always leave out some classes. Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes. We evaluate SNNs on both dermoscopic and clinical images of skin lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets, respectively. Although this is slightly lower than the state-of-the-art results, the SNN approach has the advantage that it can detect out-of-class examples. Our results highlight the potential of an SNN approach as well as pathways towards future clinical deployment.

通过研究视网膜生物结构的进展，可以识别眼病的存在和严重性是可行的。眼底检查是检查眼睛的生物结构和异常的诊断程序。诸如青光眼，糖尿病性视网膜病和白内障等眼科疾病是世界各地视觉障碍的主要原因。眼疾病智能识别（ODIR-5K）是研究人员用于多标签的多份多疾病分类的基准结构底面图像数据集。这项工作提出了一个歧视性内核卷积网络（DKCNET），该网络探讨了歧视区域的特征，而无需增加额外的计算成本。 DKCNET由注意力块组成，然后是挤压和激发（SE）块。注意块从主干网络中获取功能，并生成歧视性特征注意图。 SE块采用区分特征图并改善了通道相互依赖性。使用InceptionResnet骨干网络观察到DKCNET的更好性能，用于具有96.08 AUC，94.28 F1-SCORE和0.81 KAPPA得分的ODIR-5K底面图像的多标签分类。所提出的方法根据诊断关键字将通用目标标签拆分为眼对。基于这些标签，进行了过采样和不足采样以解决阶级失衡。为了检查拟议模型对培训数据的偏见，对ODIR数据集进行了训练的模型将在三个公开可用的基准数据集上进行测试。发现它在完全看不见的底面图像上也具有良好的性能。

皮肤癌的发病率在全世界一直在稳步上升，这是一个严重的问题。早期诊断有可能大大减少疾病造成的伤害，但是，传统活检是一种劳动密集型和侵入性的手术。此外，许多农村社区不容易获得医院，并且不希望因为他们认为可能是小问题而访问一个。使用机器学习和深度学习进行皮肤癌分类可以提高可及性，并减少传统病变检测过程中涉及的不适程序。这些模型可以包裹在网络或移动应用程序中，并为更多的人口提供服务。在本文中，在常见皮肤病变的基准HAM10000数据集上测试了两个这样的模型。它们是带有分层k折的随机森林，并且是Mobilenetv2（在其余的论文中称为Mobilenet）。使用Tensorflow和Pytorch框架分别训练Mobilenet模型。深度学习和机器学习模型的并排比较，以及对在资源约束的移动环境中针对皮肤病变诊断的不同框架的相同深度学习模型的比较。结果表明，这些模型中的每一个在不同的分类任务上都更好。为了获得更大的总回忆，准确性和恶性黑色素瘤的检测，张量流动性是更好的选择。但是，为了检测非癌性皮肤病变，Pytorch Mobilenet被证明更好。当涉及到中等正确性的计算成本低时，随机森林是更好的算法。

Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.

由于类之间的不平衡，医疗数据分类通常是一个具有挑战性的任务。在本文中，我们提出了一种方法来将Dercatospopic图像从Ham10000（具有10000次训练图像的人机）数据集进行分类，包括七种不平衡类型的皮肤病变，具有良好的精度和低资源要求。分类是通过使用佩带的卷积神经网络完成的。我们评估提案的准确性和性能，并说明可能的扩展。

由于肿胀和病态增大，人体组织中组织的异常发育被称为肿瘤。它们主要被归类为良性和恶性。大脑中的肿瘤可能是致命的，因为它可能是癌性的，因此可以以附近的健康细胞为食并不断增加大小。这可能会影响大脑中软组织，神经细胞和小血管。因此，有必要以最高的精度在早期阶段检测和分类。脑肿瘤的大小和位置不同，这使得很难理解其性质。由于附近的健康细胞与肿瘤之间的相似性，即使使用先进的MRI（磁共振成像）技术，脑肿瘤的检测和分类过程也可能是一项繁重的任务。在本文中，我们使用Keras和Tensorflow来实施最先进的卷积神经网络（CNN）架构，例如EdgitionNetB0，Resnet50，Xpection，MobilenetV2和VGG16，使用转移学习来检测和分类三种类型的大脑肿瘤，即神经胶质瘤，脑膜瘤和垂体。我们使用的数据集由3264个2-D磁共振图像和4个类组成。由于数据集的尺寸较小，因此使用各种数据增强技术来增加数据集的大小。我们提出的方法不仅包括数据增强，而且还包括各种图像降级技术，头骨剥离，裁剪和偏置校正。在我们提出的工作效率NETB0体系结构中，最佳准确性为97.61％。本文的目的是区分正常和异常像素，并以更好的准确性对它们进行分类。

在医学图像处理中，最重要的信息通常位于图像的小部分上。基于补丁的方法旨在仅使用图像中最相关的部分。寻找自动选择补丁的方法是一个挑战。在本文中，我们研究了两个选择斑块的标准：熵和光谱相似性标准。我们在不同级别的斑块大小上执行实验。我们在补丁的子集上训练卷积神经网络并分析训练时间。我们发现，除了需要减少预处理时间之外，基于熵收敛选择的贴片数据集的分类器比基于频谱相似性标准选择的斑块收敛的速度快，而且还会导致更高的精度。此外，与低熵斑块相比，高熵的斑块可导致更快的收敛性和更好的准确性。

Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, it is the most frequent cause of blindness in developed countries. Although some promising treatments have been developed, their effectiveness is low in advanced stages. This emphasizes the importance of large-scale screening programs. Nevertheless, implementing such programs for AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. All this motivates the development of automatic methods. In this sense, several works have achieved positive results for AMD diagnosis using convolutional neural networks (CNNs). However, none incorporates explainability mechanisms, which limits their use in clinical practice. In that regard, we propose an explainable deep learning approach for the diagnosis of AMD via the joint identification of its associated retinal lesions. In our proposal, a CNN is trained end-to-end for the joint task using image-level labels. The provided lesion information is of clinical interest, as it allows to assess the developmental stage of AMD. Additionally, the approach allows to explain the diagnosis from the identified lesions. This is possible thanks to the use of a CNN with a custom setting that links the lesions and the diagnosis. Furthermore, the proposed setting also allows to obtain coarse lesion segmentation maps in a weakly-supervised way, further improving the explainability. The training data for the approach can be obtained without much extra work by clinicians. The experiments conducted demonstrate that our approach can identify AMD and its associated lesions satisfactorily, while providing adequate coarse segmentation maps for most common lesions.

由于不规则的病变界限，病变与背景之间的对比度较差，以及伪影之间的对比度，皮肤病的自动分割是一种具有挑战性的任务。在这项工作中，提出了一种新的卷积神经网络的方法，用于皮肤病变分割。在这项工作中，提出了一种新型多尺度特征提取模块，用于提取更多辨别特征，以处理与复杂的皮肤病变有关的挑战;该模块嵌入在UNET中，替换标准架构中的卷积层。此外，在这项工作中，两个不同的关注机制完善了编码器提取的特征和后ups采样的特征。使用两个公开的数据集进行评估，包括ISBI2017和ISIC2018数据集。该方法报告了ISBI2017数据集中的准确性，召回和JSI，97.5％，94.29％，91.16％，95.92％，95.92％，95.37％，95.37％，91.52％在ISIC2018数据集。它在各个竞争中表现出现有的方法和排名的模型。

人工神经网络（ANN）能够学习，纠正错误和将大量原始数据转化为治疗和护理的有用医疗决策，这增加了增强患者安全和护理质量的普及。因此，本文审查了ANN的关键作用为患者医疗保健决策提供有价值的见解和有效的疾病诊断。我们彻底审查了现有文献中的不同类型的ANN，以便为复杂应用程序进行高级ANNS适配。此外，我们还调查Ann的各种疾病诊断和治疗的进步，例如病毒，皮肤，癌症和Covid-19。此外，我们提出了一种名为ConxNet的新型深度卷积神经网络（CNN）模型，用于提高Covid-19疾病的检测准确性。 ConxNet经过培训并使用不同的数据集进行测试，它达到了超过97％的检测精度和精度，这明显优于现有型号。最后，我们突出了未来的研究方向和挑战，例如算法的复杂性，可用数据，隐私和安全性，以及与ANN的生物传染集成。这些研究方向需要大幅关注改善医疗诊断和治疗应用的ANN的范围。

最近关于Covid-19的研究表明，CT成像提供了评估疾病进展和协助诊断的有用信息，以及帮助理解疾病。有越来越多的研究，建议使用深度学习来使用胸部CT扫描提供快速准确地定量Covid-19。兴趣的主要任务是胸部CT扫描的肺和肺病变的自动分割，确认或疑似Covid-19患者。在这项研究中，我们使用多中心数据集比较12个深度学习算法，包括开源和内部开发的算法。结果表明，合并不同的方法可以提高肺部分割，二元病变分割和多种子病变分割的总体测试集性能，从而分别为0.982,0.724和0.469的平均骰子分别。将得到的二元病变分段为91.3ml的平均绝对体积误差。通常，区分不同病变类型的任务更加困难，分别具有152mL的平均绝对体积差，分别为整合和磨碎玻璃不透明度为0.369和0.523的平均骰子分数。所有方法都以平均体积误差进行二元病变分割，该分段优于人类评估者的视觉评估，表明这些方法足以用于临床实践中使用的大规模评估。

皮肤病变的准确和公正检查对于早期诊断和治疗皮肤疾病至关重要。皮肤病变的视觉特征明显差异，因为图像是通过使用不同的成像设备从具有不同病变颜色和形态的患者中收集的。最近的研究报告说，结合卷积神经网络（CNN）是实用的，可以对图像进行分类以早期诊断皮肤疾病。但是，这些连接的CNN的实际使用受到限制，因为这些网络是重量级的，并且不足以处理上下文信息。尽管开发了轻量级网络（例如MobileNetV3和ExcilityNet），以减少参数来实现移动设备上的深神经网络，但功能表示深度不足会限制性能。为了解决现有的局限性，我们开发了一个新的精简神经网络，即Hierattn。 Hierattn采用了一种新颖的深度监督策略，通过使用只有一种训练损失的多阶段和多分支注意力机制来学习本地和全球特征。通过使用皮肤镜图像数据集ISIC2019和智能手机照片数据集PAD-FIFES-20（PAD2020）评估Hierattn的功效。实验结果表明，Hierattn在最先进的轻量级网络中达到了曲线（AUC）下最佳的精度和面积。该代码可从https://github.com/anthonyweidai/hierattn获得。

卷积神经网络在皮肤病变图像分类中表现出皮肤科医生水平的表现，但是由于训练数据中看到的偏见而引起的预测不规则性是在可能在广泛部署之前解决的问题。在这项工作中，我们使用两种领先的偏见未学习技术从自动化的黑色素瘤分类管道中稳健地消除了偏见和虚假变化。我们表明，可以使用这些偏置去除方法合理地减轻先前研究中介绍的手术标记和统治者引入的偏见。我们还证明了与用于捕获病变图像的成像仪器有关的杂化变异的概括优势。我们的实验结果提供了证据，表明上述偏见的影响大大降低了，不同的偏见技术在不同的任务方面具有出色的作用。

乳腺癌是女性可能发生的最严重的癌症之一。通过分析组织学图像（HIS）来自动诊断乳腺癌对患者及其预后很重要。他的分类为临床医生提供了对疾病的准确了解，并使他们可以更有效地治疗患者。深度学习（DL）方法已成功地用于各种领域，尤其是医学成像，因为它们有能力自动提取功能。这项研究旨在使用他的乳腺癌对不同类型的乳腺癌进行分类。在这项研究中，我们提出了一个增强的胶囊网络，该网络使用RES2NET块和四个额外的卷积层提取多尺度特征。此外，由于使用了小的卷积内核和RES2NET块，因此所提出的方法具有较少的参数。结果，新方法的表现优于旧方法，因为它会自动学习最佳功能。测试结果表明该模型的表现优于先前的DL方法。